Flask Developer Hiring Handbook for Growing Businesses

Key signals grounding this flask developer hiring handbook:

• Companies in the top quartile of Developer Velocity outperform the bottom quartile by 4–5x on revenue growth (McKinsey & Company).
• The global software developer population reached 28.7 million in 2024 and is projected to hit 45 million by 2030, expanding the hiring pool for backend roles (Statista).

Which core skills define a production-ready Flask developer?

A production-ready Flask developer demonstrates Python fluency, web fundamentals, scalable API craftsmanship, rigorous testing, security practice, and cloud-native delivery.

1. Python proficiency and Flask patterns

• Mastery of language features, typing, async primitives, and idiomatic Flask blueprints and factories.
• Strong grasp of request lifecycles, context locals, and extension configuration across environments.
• Enables maintainable endpoints, predictable behavior, and efficient code reviews in hiring backend developers.
• Reduces defects and accelerates onboarding during tech team expansion and scaling flask teams.
• Applies type hints, linting, and structured logging to keep services observable and dependable.
• Leverages app factories, CLI commands, and config objects to standardize service setup.

2. RESTful API design and data serialization

• Resource modeling, pagination, filtering, and error contracts with JSON:API or OpenAPI alignment.
• Marshmallow or Pydantic for schema validation and consistent payload structures across services.
• Promotes stable interfaces, cleaner integrations, and safer client evolution in startup hiring guide contexts.
• Eases partner onboarding and lowers breaking-change risk during flask staffing strategy iterations.
• Implements versioning, ETags, idempotency keys, and problem+json error formats.
• Generates docs via Swagger/UI or ReDoc for zero-ambiguity integration paths.

3. SQLAlchemy and database performance

• ORM mastery, migrations with Alembic, and query optimization strategies for relational data.
• Connection pooling, indexing, and transaction boundaries tuned for throughput and integrity.
• Sustains predictable latency under load, aiding scaling flask teams to meet SLO targets.
• Limits cloud spend by cutting N+1 queries and inefficient scans during tech team expansion.
• Uses EXPLAIN plans, profiling, and data access layers to isolate persistence concerns.
• Applies read replicas, caching hints, and retry semantics for resilience.

4. Asynchronous tasks with Celery and queues

• Background jobs, scheduled tasks, and eventual consistency patterns over Redis or RabbitMQ.
• Reliable processing for emails, webhooks, ETL, and long-running workflows.
• Shields APIs from latency spikes, improving uptime during hiring backend developers ramp.
• Increases throughput while keeping request handlers lean and deterministic.
• Configures retries, dead-letter queues, and idempotent task design for safety.
• Observes workers via metrics, tracing, and alerting tied to SLA thresholds.

5. Testing practices and CI pipelines

• Unit, integration, and contract tests with pytest, coverage, and factory fixtures.
• Mocking I/O boundaries and seeding predictable test data states.
• Prevents regressions and supports rapid merges when scaling flask teams.
• Builds trust for cross-functional launches within a startup hiring guide framework.
• Enforces quality gates, parallelization, and flaky-test quarantine in CI.
• Publishes artifacts, SBOMs, and signed images as part of release hygiene.

6. Containerization and cloud deployment

• Docker images, multi-stage builds, and minimal base layers for security.
• Orchestration with ECS, EKS, or GKE and autoscaling policies.
• Speeds delivery and rollback safety during tech team expansion initiatives.
• Standardizes runtime parity, cutting environment drift in flask staffing strategy rollouts.
• Uses WSGI/ASGI servers, health checks, and readiness probes for stability.
• Bakes in env-config, secrets mounting, and runtime resource limits.

Plan your first senior Flask hire with role-aligned scorecards

Do Flask projects benefit from specific backend architectures for scale?

Flask projects benefit from specific backend architectures for scale, emphasizing modularity, observability, and performance controls.

1. Modular blueprint architecture

• Separation by domain with blueprints, dependency injection, and clear ownership.
• Shared middlewares and extensions wrapped with stable interfaces.
• Enables parallel workstreams and safer refactors during tech team expansion.
• Minimizes merge conflicts and cognitive load when scaling flask teams.
• Uses package boundaries, app factory wiring, and per-domain error mappers.
• Applies health endpoints and per-module configs for targeted tuning.

2. Service decomposition and microservices

• Bounded contexts split by coupling, throughput, and risk profiles.
• Protocol choices across REST, gRPC, and events for clear contracts.
• Reduces blast radius and unlocks independent deploys for hiring backend developers.
• Aligns roadmaps to team autonomy and latency objectives.
• Implements service mesh, discovery, and rate controls for safe growth.
• Introduces governance for versioning, SLOs, and incident handoffs.

3. Caching layers and rate limiting

• Redis-backed caches, request coalescing, and token buckets.
• Strategic TTLs, cache keys, and invalidation triggers.
• Cuts origin load and boosts P95 latency during spikes.
• Protects core databases as traffic scales under startup hiring guide targets.
• Adds CDN, reverse proxy caching, and local memoization patterns.
• Enforces per-IP, per-token, and per-endpoint quotas.

4. API gateways and versioning

• Central ingress with authN, authZ, and traffic shaping.
• Declarative routing and schema governance at the edge.
• Simplifies client integration and backward compatibility promises.
• Shields internals as flask staffing strategy evolves over releases.
• Rolls canary versions, blue/green, and shadow traffic with metrics.
• Maintains deprecation schedules and migration guides.

5. Observability stack

• Logs, traces, and metrics stitched with correlation IDs.
• Dashboards for latency, saturation, and error classes.
• Speeds root-cause analysis during incidents across teams.
• Improves on-call confidence and MTTD/MTTR for tech team expansion.
• Uses OpenTelemetry, structured logs, and exemplars for clarity.
• Wires SLO burn alerts and runbook links to pages.

6. Horizontal scaling with WSGI/ASGI

• Gunicorn or uWSGI tuning, workers per core, and async options.
• Load balancers with sticky sessions avoided for stateless purity.
• Delivers linear capacity additions with predictable cost curves.
• Sustains concurrency without sacrificing response integrity.
• Sets keep-alive, backlog, and timeout values by traffic shape.
• Validates with load tests, chaos drills, and blue/green rollouts.

Design a scalable Flask platform blueprint with our architects

Who should own hiring backend developers for a Flask codebase?

Hiring backend developers for a Flask codebase should be owned by an engineering hiring manager supported by a calibrated technical panel and an enabled recruiter.

1. Hiring manager responsibilities

• Role definition, leveling, and business alignment for success criteria.
• Prioritized competencies and compensation guardrails.
• Keeps interviews consistent and tied to outcomes in startup hiring guide contexts.
• Accelerates decisions while preserving bar quality.
• Creates scorecards, owns final call, and ensures feedback hygiene.
• Partners with finance and HR on headcount plans.

2. Technical screening panel composition

• Mix of senior ICs, staff engineers, and a security-minded reviewer.
• Balanced expertise across APIs, data, and ops.
• Yields trusted readouts on strengths and risks for flask staffing strategy.
• Reduces false negatives while avoiding halo bias.
• Rotates members, trains on rubrics, and records calibrations.
• Enforces time-boxed, scenario-driven assessments.

3. Recruiter enablement and scorecards

• Kickoff docs with JD, EVP, and must-haves vs. nice-to-haves.
• Library of prompts and red flags per competency.
• Improves pipeline quality and throughput for tech team expansion.
• Shortens time-to-fill while preserving candidate experience.
• Uses structured scorecards mapped to leveling guides.
• Sends tight summaries that speed hiring manager reviews.

4. Interview calibration and rubrics

• Shared benchmarks, sample answers, and pass/fail anchors.
• Shadowing rounds and back-to-back debriefs.
• Drives fairness and signal clarity across scaling flask teams.
• Prevents question drift and duplication across loops.
• Uses numeric anchors and evidence-based notes only.
• Iterates rubrics quarterly based on outcome metrics.

5. Bar-raiser or SME role

• Independent reviewer focused on long-term standards.
• Escalation path when panels split on outcomes.
• Safeguards culture and technical bar during hiring backend developers.
• Blocks rushed exceptions that risk later churn.
• Audits process health and interviewer performance.
• Mentors panelists to remove bias and noise.

6. Decision timelines and SLAs

• Clear clocks for feedback, debrief, and offer approvals.
• Dashboarding for funnel health and stuck candidates.
• Limits drop-off and reneges in competitive markets.
• Keeps momentum aligned to sprint and release cycles.
• Tracks time-to-hire, offer-acceptance, and source mix.
• Publishes weekly status to stakeholders.

Stand up a calibrated Flask hiring loop in two weeks

Can a startup hiring guide streamline interviews for Flask roles?

A startup hiring guide can streamline interviews for Flask roles by standardizing role definitions, loops, artifacts, and close plans.

1. Role definition and leveling

• Responsibilities, scope, and impact expectations per level.
• Core stack, adjacent tools, and constraints upfront.
• Focuses screens on signal, not trivia, across scaling flask teams.
• Aligns comp, progression, and coaching paths.
• Ships JD templates, leveling matrices, and EVP snippets.
• Aligns panel expertise to competencies per level.

2. Structured interview loops

• Sequence of API, data, design, and behavioral rounds.
• Time-boxed segments with clear outcomes.
• Raises consistency and fairness in hiring backend developers.
• Enables apples-to-apples comparisons at debrief.
• Uses shared prompts, timing cards, and anchors.
• Records evidence, not impressions, in ATS.

3. Take-home or live coding design

• Realistic tasks mirroring production constraints.
• Open-ended edges to probe judgment and tradeoffs.
• Surfaces practical skill over memorization for startup hiring guide goals.
• Protects candidate time with clear scoping and SLAs.
• Provides sample datasets, fixtures, and seed repos.
• Reviews against rubric with reproducible criteria.

4. System design prompts aligned to Flask

• Throughput, latency, and data shape tailored to API needs.
• Tradeoffs across caching, queues, and storage.
• Validates readiness for tech team expansion realities.
• Shows depth in observability, resiliency, and security.
• Presents capacity targets, SLOs, and failure modes.
• Scores structure, clarity, and risk thinking.

5. Behavioral signals for startup fit

• Ownership, curiosity, and bias for action indicators.
• Conflict resolution, feedback loops, and autonomy.
• Reduces culture debt during flask staffing strategy growth.
• Improves retention and cross-team collaboration.
• Uses STAR notes and follow-up probes consistently.
• Flags anti-patterns like blame, vagueness, or rigidity.

6. Offer process and closing tactics

• Competitive comp bands, equity education, and perks.
• Timeline clarity and ramp expectations.
• Lifts acceptance rates across scaling flask teams.
• Reduces reneges with transparent start plans.
• Creates closing narratives tied to mission and impact.
• Aligns references, approvals, and written offers fast.

Use our interview kits tailored to Flask backends

Is a flask staffing strategy different for greenfield vs. legacy systems?

A flask staffing strategy differs for greenfield vs. legacy systems by emphasizing discovery and modernization tracks matched to risk and throughput.

1. Greenfield team topology

• Small cross-functional squad with clear domain ownership.
• Product, backend, and platform roles aligned to a single backlog.
• Maximizes velocity and learning loops in startup hiring guide stages.
• Limits coordination overhead and dependency chains.
• Chooses generalists comfortable across API, data, and infra.
• Builds paved paths early to de-risk growth.

2. Legacy modernization squads

• Dedicated team for refactors, strangler patterns, and deprecations.
• SLAs maintained while peeling modules into cleaner seams.
• Contains blast radius during tech team expansion.
• Prevents feature freezes in adjacent domains.
• Staffs with engineers experienced in brownfield constraints.
• Partners with QA and SRE for safe migrations.

3. Migration roadmap and milestones

• Inventory services, dependencies, and data contracts.
• Sequence workloads by value, effort, and risk.
• Sets expectations for stakeholders in flask staffing strategy rollouts.
• Aligns funding tranches to delivered milestones.
• Establishes exit criteria, gates, and rollback points.
• Tracks burn rate and earned value transparently.

4. Risk management and rollout plans

• Failure modes, guardrails, and contingency playbooks.
• Canary, shadow, and staged migrations.
• Protects revenue and uptime while scaling flask teams.
• Builds trust through predictable change windows.
• Formalizes approvals, sign-offs, and change logs.
• Simulates incidents with game days.

5. Backward compatibility and contracts

• Versioned APIs, schema evolution, and consumer-driven contracts.
• Tolerant readers and explicit deprecation windows.
• Lowers integration churn for hiring backend developers outputs.
• Keeps partners unblocked across release cycles.
• Uses Pact tests, feature flags, and staged rollouts.
• Publishes migration guides and linters for drift.

6. Budget allocation and capacity planning

• Fixed vs. variable cost modeling across vendors and hires.
• Reserve capacity for incident and tech debt buffers.
• Balances runway with delivery pressure in startup hiring guide phases.
• Prevents overstaffing prior to product-market fit.
• Uses throughput forecasts and utilization targets.
• Reviews quarterly to adjust ratios and spend.

Map greenfield vs. legacy staffing with a scenario plan

Are security and compliance non-negotiable in Flask team expansion?

Security and compliance are non-negotiable in Flask team expansion, with baseline controls embedded across code, pipelines, and runtime.

1. OWASP and Flask-specific controls

• Input validation, CSRF, CORS, and template safety.
• Rate limits, payload caps, and output encoding.
• Cuts exploit surface as services multiply during tech team expansion.
• Satisfies baseline due diligence for regulated clients.
• Applies secure cookies, session flags, and request size guards.
• Audits dependencies and pins versions in lockfiles.

2. Secrets management and config hygiene

• Centralized vaulting, short-lived tokens, and zero secrets in code.
• Strict least privilege across envs and services.
• Prevents leaks during scaling flask teams and vendor onboarding.
• Simplifies rotation and incident containment.
• Uses KMS, Vault, and sealed secrets in CI/CD.
• Enforces Git hooks and scanners to block exposures.

3. Authentication and authorization stacks

• Standards-driven authN with OAuth2/OIDC and SSO.
• Role and attribute-based access control with audits.
• Protects data paths while hiring backend developers in parallel.
• Enables partner integration with minimal friction.
• Implements token expiry, refresh flows, and revocation.
• Logs grants, denials, and admin actions for evidence.

4. Data privacy and regional regulations

• Data mapping, retention, and subject rights processes.
• Regional storage, residency, and cross-border rules.
• Reduces legal exposure under flask staffing strategy growth.
• Improves client trust and procurement velocity.
• Pseudonymizes datasets and limits PII in lower envs.
• Documents processing bases and DPA terms.

5. Secure SDLC and threat modeling

• Gates for code scanning, SCA, and container checks.
• Architecture reviews anchored on attack paths.
• Prevents regressions as teams split during tech team expansion.
• Bakes security into delivery, not as a late stage.
• Uses STRIDE-style analyses and abuse case catalogs.
• Tracks findings to closure with SLA dashboards.

6. Incident response and runbooks

• Clear roles, comms channels, and escalation ladders.
• Post-incident reviews with action ownership.
• Cuts MTTD/MTTR as scaling flask teams grow surface area.
• Preserves customer trust through transparent updates.
• Runbooks for DDoS, credential theft, and data loss.
• Drills quarterly with realistic failure injects.

Embed security controls into your Flask delivery pipeline

Will tech team expansion require changes to processes and tooling?

Tech team expansion requires changes to processes and tooling to maintain velocity, reliability, and quality at scale.

1. Git branching and code review policy

• Trunk-based or short-lived branches with protected main.
• Mandatory reviews, small PRs, and template checklists.
• Keeps flow stable as repos and contributors increase.
• Raises signal quality without blocking delivery.
• Adds CODEOWNERS, automations, and required checks.
• Enforces semantic commits and conventional PR labels.

2. CI/CD pipelines and quality gates

• Fast feedback, parallel jobs, and caching layers.
• Policy-as-code for tests, coverage, and security.
• Preserves confidence during frequent releases.
• Enables safe experimentation under startup hiring guide aims.
• Uses reusable workflows and environment promotions.
• Attaches SBOMs, signatures, and provenance attestations.

3. Environment parity and infra-as-code

• Dev, staging, and prod mirrors with minimal drift.
• Terraform or Pulumi with reviewable diffs.
• Prevents configuration snowflakes at team scale.
• Simplifies disaster recovery and onboarding.
• Ships modules, versioned stacks, and change plans.
• Validates with drift detection and policy checks.

4. Docs and architecture decision records

• Living READMEs, runbooks, and diagrams per service.
• ADRs to preserve context behind choices.
• Reduces rediscovery costs during tech team expansion.
• Eases cross-team collaboration and ownership changes.
• Templates seeded in repos with linted structure.
• Links to dashboards, SLOs, and contacts.

5. Onboarding playbooks and mentorship

• 30/60/90 plans, buddy systems, and lab days.
• Pre-provisioned access and golden paths.
• Speeds time-to-impact when hiring backend developers at pace.
• Builds culture of teaching and durable craftsmanship.
• Provides starter issues, shadowing, and feedback loops.
• Tracks ramp KPIs to refine cohorts.

6. Capacity planning and sprint rituals

• Throughput trends, utilization caps, and buffers.
• Quarterly goals aligned to staffing and funding.
• Matches scope to capacity in scaling flask teams.
• Avoids burnout and deadline slips.
• Calibrates sprint lengths, WIP limits, and cadences.
• Reviews retros with actionable improvements.

Operationalize scale with right-sized delivery practices

Should you hire generalists or specialists for scaling Flask teams?

You should balance generalists and specialists for scaling Flask teams based on product stage, risk, and performance goals.

1. Generalist profiles and scope

• Broad backend skills across APIs, data, and DevOps.
• Comfortable with ambiguity and rapid shifts.
• Boosts early velocity in startup hiring guide phases.
• Reduces handoffs and coordination overhead.
• Takes end-to-end ownership from design to deploy.
• Adapts stack choices to evolving constraints.

2. Specialist roles and deep expertise

• Performance, security, data engineering, or SRE focus.
• Advanced tuning, audits, and reliability methods.
• Addresses scaling chokepoints as traffic climbs.
• Raises resilience for regulated or mission-critical flows.
• Tunes queries, profiles latency, and hardens auth.
• Leads playbooks and incident prevention programs.

3. Hybrid staffing models

• Pods mixing generalists with targeted specialists.
• Rotations to spread knowledge and reduce silos.
• Balances throughput with depth during tech team expansion.
• Keeps quality high without stalling features.
• Defines charters, interfaces, and shared KPIs.
• Funds flex capacity for spikes and audits.

4. Org stages and hiring ratios

• Pre-PMF: majority generalists; seed specialist advisory.
• Post-PMF: increase specialists as scale pains appear.
• Aligns spend to ROI for flask staffing strategy outcomes.
• Avoids premature optimization that slows delivery.
• Reviews ratios quarterly against incident and latency data.
• Adjusts leveling mix to pipeline realities.

5. Compensation bands and career paths

• Clear ladders for IC and management tracks.
• Market-calibrated ranges with equity philosophy.
• Attracts senior talent in hiring backend developers cycles.
• Retains expertise through predictable growth.
• Documents competencies, expectations, and promotion gates.
• Benchmarks annually with trusted surveys.

6. Rotation and knowledge sharing

• Regular rotations across services and domains.
• Internal talks, demos, and design clinics.
• Builds resilience as scaling flask teams evolve.
• Prevents single points of failure and burnout.
• Schedules shadowing for on-call and incident roles.
• Captures lessons in ADRs and runbooks.

Calibrate your generalist-to-specialist mix with market data

Can contractor networks accelerate time-to-hire for Flask initiatives?

Contractor networks can accelerate time-to-hire for Flask initiatives when vendors, contracts, and knowledge transfer are tightly governed.

1. Vendor selection and due diligence

• Portfolio reviews, reference checks, and trial sprints.
• Security posture and compliance evidence upfront.
• Filters noise while scaling flask teams under deadlines.
• Improves delivery confidence for regulated scopes.
• Scores vendors on skills, throughput, and quality.
• Pilots with clear exit and success criteria.

2. Contract models and SLAs

• Time-and-materials, fixed-scope, or retainer blends.
• SLOs for quality, velocity, and communication.
• Aligns incentives during hiring backend developers surges.
• Reduces disputes through measurable outcomes.
• Sets governance cadence, artifacts, and escalation ladders.
• Limits scope creep via change control.

3. Knowledge transfer and retention

• Paired delivery, docs, and recorded walkthroughs.
• Shadowing for handover and embedded playbooks.
• Prevents brain drain after sprints complete.
• Guards continuity as teams rebalance capacity.
• Agrees on code ownership and contribution rules.
• Schedules overlap for post-go-live support.

4. Security vetting and access controls

• Background checks, NDA, and least-privilege access.
• Segmented envs and short-lived credentials.
• Protects assets as vendor footprint expands.
• Meets audit demands during flask staffing strategy pushes.
• Uses SSO, MFA, and just-in-time approvals.
• Revokes access via automated offboarding.

5. Cost modeling and ROI tracking

• Rate cards, blended rates, and ramp curves.
• Burndown vs. delivered value dashboards.
• Confirms payoff for tech team expansion spend.
• Cuts waste with early course corrections.
• Compares vendor output with internal baselines.
• Feeds learning into next contract cycle.

6. Multi-vendor coordination

• Single intake, playbooks, and integration contracts.
• Shared standards for code, docs, and pipelines.
• Prevents drift and overlap across scaling flask teams.
• Improves predictability of cross-vendor deliverables.
• Appoints a lead vendor or internal integration owner.
• Schedules joint demos and milestone gates.

Spin up vetted Flask contractors with clear SLAs

Do KPIs prove value from Flask developer hiring investments?

KPIs prove value from Flask developer hiring investments by connecting delivery, reliability, quality, funnel, cost, and business impact metrics.

1. Delivery metrics and lead time

• Commit-to-prod, deployment frequency, and batch size.
• Change failure rate tied to release cadence.
• Shows throughput shifts from hiring backend developers.
• Validates sustainable pace under startup hiring guide objectives.
• Tracks trends per team and per service.
• Flags regressions to trigger coaching or tooling.

2. Reliability metrics and error rates

• Availability SLOs, incident counts, and MTTR.
• P95/P99 latency and saturation indicators.
• Reflects resilience gains from scaling flask teams.
• Links operational health to customer experience.
• Segments by endpoint, dependency, and region.
• Anchors on user-facing impact, not vanity stats.

3. Quality metrics and defect escape

• Pre-prod defect rates vs. prod escapes.
• Flaky tests and recovery time from broken builds.
• Captures code health across flask staffing strategy changes.
• Drives targeted investments in testing and tooling.
• Uses triage tags and ownership for clarity.
• Publishes dashboards visible to all squads.

4. Hiring funnel metrics

• Source mix, pass-through rates, and time-to-offer.
• Offer-acceptance and ramp time to first impactful PR.
• Exposes pipeline strengths in tech team expansion.
• Informs recruiter focus and panel calibration.
• Ties signals to retention and performance after hire.
• Benchmarks quarterly against market movement.

5. Cost metrics and unit economics

• Cost per hire, onboarding cost, and run-rate impact.
• Infra and tooling spend per request or per user.
• Clarifies ROI across startup hiring guide phases.
• Supports budget reallocation to high-return levers.
• Tracks savings from defect reduction and stability.
• Compares build vs. buy vs. vendor outcomes.

6. Business impact and proxy outcomes

• Revenue throughput, activation, and churn deltas.
• Feature cycle time tied to conversion or retention.
• Grounds hiring backend developers decisions in impact.
• Justifies headcount with goal-linked evidence.
• Uses guardrail metrics for risk and debt.
• Reports quarterly with narrative and data.

Instrument the right KPIs to validate hiring ROI

Faqs

1. Which skills should a Flask backend hire demonstrate?

• Proficiency in Python, Flask patterns, SQLAlchemy, testing, security fundamentals, and containerized deployment.

2. Is async expertise essential for Flask roles today?

• Beneficial for I/O-heavy services and event-driven tasks, though many APIs succeed with well-tuned sync stacks.

3. Can small startups scale Flask teams without heavy overhead?

• Yes, by standardizing repos, CI/CD, and interviews, then layering specialists only when bottlenecks appear.

4. Do microservices outperform monoliths for every Flask project?

• No, start modular; split services once bounded contexts, throughput needs, and team capacity justify it.

5. Should contractors be used for urgent Flask backlogs?

• Yes, if SLAs, security vetting, and knowledge transfer plans are explicit from day one.

6. Are security and compliance baseline skills for hires?

• Yes, candidates must demonstrate input validation, authZ/authN, secrets hygiene, and logging discipline.

7. Will KPIs confirm hiring ROI on Flask initiatives?

• Yes, track lead time, failure rates, defect escape, cost per hire, and business impact proxies.

8. Do generalists or specialists fit better in early teams?

• Generalists early for velocity; specialists later to deepen performance, data, and security capabilities.

Sources

• https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/developer-velocity-how-software-excellence-fuels-business-performance
• https://www.statista.com/statistics/165564/number-of-software-developers-worldwide/
• https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work.html