Databricks Engineer Job Description (Ready-to-Use Template)
Databricks Engineer Job Description (Ready-to-Use Template)
- Data-driven leaders see outsized outcomes: 23x customer acquisition, 6x retention, 19x profitability (McKinsey & Company), elevating the need for a databricks engineer job description.
- Scaling AI platforms can drive up to 20% EBIT uplift for mature adopters (Boston Consulting Group), reinforcing platform engineering investment.
Which core responsibilities define a Databricks engineer role?
The core responsibilities that define a Databricks engineer role span pipeline design, Spark optimization, Delta governance, automation, cost, and reliability. These databricks engineer roles responsibilities guide hiring criteria and scope.
1. Lakehouse pipeline design
- End-to-end ingestion, transformation, and modeling across bronze, silver, and gold layers on Delta Lake.
- Modular data assets standardize reuse, accelerate delivery, and reduce maintenance risk.
- Design patterns align with medallion architecture, data contracts, and versioned outputs.
- Reliability improves through idempotent jobs, retries, and checkpointed stages.
- Implement CDC, batch, and streaming paths with schema evolution and validation.
- Promote notebooks and code to Jobs with parameterization and environment parity.
2. Spark job tuning and optimization
- Techniques for partitioning, caching, and efficient join strategies within Spark.
- Performance gains cut cost, shrink runtimes, and raise SLA confidence.
- Manage shuffle, skew, and file sizes using adaptive query execution and hints.
- Benchmarks validate settings for executor sizing, autoscaling, and AQE thresholds.
- Apply broadcast joins, Z-ordering, and compaction to reduce I/O overhead.
- Profiling with Spark UI and Ganglia guides targeted fixes over guesswork.
3. Delta Lake governance and data reliability
- ACID transactions, time travel, and schema enforcement across tables.
- Consistency protects critical analytics, ML features, and regulatory reporting.
- Use constraints, expectations, and optimize commands for stable datasets.
- Retention and vacuum policies balance performance with compliance needs.
- Handle DLT expectations and deduplication to maintain data integrity.
- Recovery via restore points and transaction logs reduces incident impact.
4. Workflow orchestration and automation
- Coordinated Jobs, tasks, and triggers across batch and streaming workloads.
- Orchestration reduces manual effort and enforces dependency order.
- Compose tasks with task orchestration, dbutils widgets, and job clusters.
- External schedulers integrate via REST APIs, Airflow, or cloud-native tools.
- Parameterize runs for environments, secrets, and tenants with consistent naming.
- Notifications, retries, and SLAs codify operational discipline end-to-end.
Scope Databricks responsibilities into a clear JD and align on ownership
Which skills are required for a Databricks engineer in 2026?
The skills required for a Databricks engineer in 2026 center on PySpark, SQL, streaming, automation, cloud fluency, and security. Map each skill directly to the databricks engineer job description and outcomes.
1. PySpark and SQL mastery
- Dataframe APIs, Window functions, UDF avoidance, and SQL idioms for scale.
- Strong foundations cut complexity and lift maintainability under load.
- Vectorized operations and Catalyst-friendly queries reduce compute waste.
- Query plans guide improvements to joins, filters, and projections.
- Reproducible notebooks and modular packages support code reuse.
- Unit tests with pytest and SQL-based checks secure correctness.
2. Structured Streaming and Delta Live Tables
- Continuous processing with stateful aggregations and event-time semantics.
- Real-time insights unlock timely actions and fresher decision cycles.
- Configure triggers, watermarks, and checkpoints for resilient flows.
- DLT pipelines codify dependencies, expectations, and quality gates.
- Recoverability stems from checkpoint hygiene and idempotent design.
- Backfill plans and versioned configs enable safe reprocessing.
3. CI/CD with Repos, Git, and Terraform
- Version control, review gates, and infrastructure as code for Databricks.
- Automation shortens cycle time and reduces drift across workspaces.
- Repos integrate with Git providers and enforce branch policies.
- Terraform manages clusters, pools, permissions, and workspace objects.
- Pipelines promote notebooks, wheels, and SQL artifacts across stages.
- Secrets, variables, and approvals secure releases without bottlenecks.
4. Cloud platform fluency across AWS, Azure, GCP
- Storage tiers, networking, identity, and logging patterns per cloud.
- Cloud alignment boosts security stance and cost predictability.
- Configure VPC/VNet rules, private links, and secure cluster connectivity.
- Choose storage classes, lifecycle rules, and encryption defaults.
- Map IAM roles, service principals, and SCIM groups to Unity Catalog.
- Centralize logs and metrics via CloudWatch, Monitor, or Cloud Logging.
Build a skills-aligned hiring plan and map this JD to delivery milestones
Which tools and frameworks should a Databricks engineer know?
The tools and frameworks a Databricks engineer should know include Unity Catalog, Databricks SQL, MLflow, and orchestration options. Tool depth aligns the databricks developer JD template with platform standards.
1. Unity Catalog and fine-grained access
- Centralized governance for data, AI assets, and permissions.
- Unified control reduces risk and accelerates onboarding.
- Apply catalogs, schemas, and grants with least privilege.
- Attribute-based policies tailor access by data sensitivity.
- Lineage graphs surface dependencies across the stack.
- Audits and diagnostics confirm policy intent and coverage.
2. MLflow and Model Registry
- Experiment tracking, model packaging, and lifecycle management.
- Consistent ML delivery links research with production.
- Log parameters, metrics, and artifacts for traceability.
- Register, version, and transition models across stages.
- Integrate inference with batch scoring and serving endpoints.
- Rollbacks and canaries mitigate deployment risk.
3. Databricks SQL and BI integration
- SQL endpoints, dashboards, and governance-aware queries.
- Self-serve analytics expands reach without sprawl.
- Tune warehouses for concurrency and elasticity.
- Connect BI tools through JDBC/ODBC with SSO.
- Materialize views and incremental patterns for performance.
- Caching and query policies stabilize interactive usage.
Which KPIs define success for a Databricks engineer?
The KPIs that define success for a Databricks engineer track reliability, efficiency, speed, and quality. Tie each KPI to ownership in the databricks engineer job description.
1. SLA adherence and pipeline uptime
- Availability targets for critical jobs and datasets.
- Reliable services earn stakeholder trust at scale.
- Measure success rate, MTTD, and MTTR across tiers.
- Incident postmortems drive durable improvements.
- Error budgets guide release pace and risk tolerance.
- Runbooks and alerts reduce toil and uncertainty.
2. Cost efficiency and cluster utilization
- Spend per workload, per table, and per user segment.
- Clear economics align engineering with business value.
- Right-size clusters, pools, and autoscaling behavior.
- Optimize file layout, caching, and compression choices.
- Track DBUs, storage, and egress with tagged workloads.
- Budgets and forecasts anchor quarterly planning.
3. Data latency and freshness SLIs
- End-to-end time from source events to curated tables.
- Faster cycles enable timely decisions and actions.
- Stream-to-batch bridges stabilize near-real-time use cases.
- Windowing and watermarking keep state consistent.
- Backfill throughput targets prevent backlog growth.
- Freshness dashboards confirm expectations daily.
Define role KPIs in the JD and align reviews to measurable outcomes
Who should use this databricks developer JD template?
The databricks developer JD template serves hiring managers, founders, and enterprise leaders building data products. Use it to standardize fit, level, and scope.
1. Digital-native startups scaling analytics
- Fast-moving teams building product telemetry and growth loops.
- A shared template avoids role confusion and mis-hires.
- Scope covers ingestion, modeling, and near-real-time analytics.
- Ownership spans code, jobs, and observability from day one.
- Clear expectations de-risk on-call and escalation paths.
- Leveling guides comp, progression, and mentoring plans.
2. Mid-market teams modernizing warehouses
- Organizations shifting from ETL appliances to Lakehouse.
- A consistent JD anchors change across squads and quarters.
- Responsibilities align migration waves and deprecation timelines.
- Dual-run plans protect reporting during transitions.
- Quality gates prevent regressions across KPIs and SLAs.
- Training paths accelerate platform adoption and reuse.
3. Global enterprises consolidating platforms
- Multi-region estates unifying governance and tooling.
- A standard JD streamlines hiring across geographies.
- Federated teams align on catalogs, lineage, and policies.
- Portfolio roadmaps coordinate ingestion, ML, and BI.
- Security baselines reduce audit findings at scale.
- Vendor and FinOps alignment keeps costs predictable.
Can you share a ready-to-use Databricks Engineer Job Description template?
Yes, a ready-to-use Databricks Engineer Job Description template appears below for direct copy and adaptation. Insert it into role pages, scorecards, and requisitions.
1. Role summary
- Own Lakehouse pipelines, Delta Lake quality, and Spark performance for data and ML workloads.
- The summary sets scope, seniority, and impact for candidates and reviewers.
- Deliver reliable, cost-efficient datasets and features across batch and streaming.
- Outcomes link to product metrics, governance, and platform maturity.
- Collaborate with data scientists, analytics engineers, and platform teams.
- Team interfaces clarify decision rights and service boundaries.
2. Key responsibilities
- Design and maintain medallion pipelines with robust testing and observability.
- Responsibilities translate business needs into repeatable engineering patterns.
- Tune Spark jobs, partitioning, and storage layouts for stability and speed.
- Improvements reduce spend, runtime variance, and defect rates.
- Enforce Unity Catalog policies, lineage, and data protection controls.
- Governance embeds compliance while enabling safe self-service.
3. Required qualifications
- Strong Python, PySpark, and SQL with production Spark delivery.
- Baseline capabilities ensure readiness for critical workloads.
- Hands-on Delta Lake, Structured Streaming, and Databricks Jobs.
- Depth enables reliable tables, features, and real-time paths.
- CI/CD with Git and Terraform plus cloud provider fluency.
- Automation skills support scalable, secure, and auditable releases.
4. Preferred qualifications
- Experience with MLflow, Feature Store, and model deployment.
- Broader exposure connects data and ML teams effectively.
- Exposure to Airflow or Workflows, and event-driven designs.
- Additional tools expand orchestration and integration choices.
- Certifications: Databricks Data Engineer, cloud data credentials.
- Recognized signals support screening and growth potential.
5. Tools and technologies
- Databricks SQL, Unity Catalog, Delta Live Tables, MLflow.
- Tool alignment streamlines onboarding and daily delivery.
- AWS/Azure/GCP storage, IAM, networking, and logging stacks.
- Platform fluency reduces friction across environments.
- Terraform, pytest, Great Expectations, and monitoring stacks.
- Quality and IaC tools reinforce reliability and governance.
6. Screening assignment
- Optimize a flawed Spark job and stabilize a Delta table with constraints.
- A focused task reveals execution depth under realistic constraints.
- Provide a small dataset, flaky logic, and skewed joins to repair.
- The setup exposes tuning, testing, and incident handling strengths.
- Request metrics, a short write-up, and a diff of code changes.
- Deliverables demonstrate clarity, trade-offs, and reproducibility.
Publish this Databricks Engineer JD and start sourcing matched candidates
Which interview questions validate Databricks depth?
The interview questions that validate Databricks depth probe Spark tuning, Delta internals, and streaming guarantees. Combine scenario prompts with hands-on tasks.
1. Spark optimization trade-offs
- Scenarios covering skew, shuffle, join strategies, and memory pressure.
- Discussions surface mental models and practical judgment.
- Evaluate proposals for partitioning schemes and caching plans.
- Responses indicate cost awareness and SLA alignment.
- Ask for instrumentation and rollback plans under risk.
- Artifacts confirm disciplined, testable, and observable changes.
2. Delta Lake transactions and schema evolution
- Topics include ACID semantics, constraints, and table services.
- Mastery prevents data corruption and reporting drift.
- Request approaches to updates, merges, and reprocessing.
- Evaluate conflict handling, compaction, and indexing choices.
- Probe migration steps for evolving schemas safely.
- Evidence shows readiness for compliance and audits.
3. Streaming reliability and recovery
- Focus on checkpoints, idempotency, late data, and state stores.
- Robust designs resist data loss and duplicated events.
- Probe checkpoint hygiene and backfill coordination.
- Evaluate watermarking choices and memory tuning.
- Ask for incident response during source instability.
- Signals include clear SLIs, runbooks, and alert design.
Calibrate interviews with hands-on tasks tied to your production stack
Which compliance and security practices must be enforced?
The compliance and security practices that must be enforced cover governance, privacy, auditing, and monitoring. Embed controls into jobs, clusters, and catalogs.
1. Access control with Unity Catalog
- Central roles, grants, and attribute-based policies across assets.
- Unified control reduces risk and audit effort.
- Apply least privilege and break-glass protocols by design.
- Periodic reviews detect drift and excessive permissions.
- Separate duties for admins, owners, and service principals.
- Logs verify policy intent and remediate gaps promptly.
2. Data protection for PII and PHI
- Masking, tokenization, encryption, and retention standards.
- Strong safeguards prevent leakage and legal exposure.
- Classify data, enforce policies, and restrict exports.
- Build shared views for sensitive fields with governed access.
- Integrate DLP scanners and secret rotation workflows.
- Validate controls through tests and sampled audits.
3. Audit, lineage, and monitoring
- End-to-end visibility across jobs, tables, models, and dashboards.
- Traceability speeds incident response and root-cause analysis.
- Emit metrics, logs, and lineage to centralized stores.
- Dashboards track SLIs, errors, and policy violations.
- On-call procedures escalate incidents with clear roles.
- Periodic chaos drills harden recovery and readiness.
Validate governance requirements and align the JD with control owners
Where does a Databricks engineer fit within a data team?
A Databricks engineer fits at the core of the data platform, bridging ingestion, modeling, ML, and analytics enablement. This placement streamlines ownership in the hiring JD databricks.
1. Partnership with data scientists
- Shared feature definitions, reproducible experiments, and model paths.
- Collaboration shortens cycles from research to production.
- Provide curated datasets, feature stores, and training pipelines.
- Co-own SLAs for inference and batch scoring flows.
- Instrument models with drift and performance metrics.
- Feedback loops drive dataset and feature quality upgrades.
2. Enablement for analytics engineers
- Governed layers, semantic models, and discoverable assets.
- Enablement expands reliable self-service analytics.
- Maintain gold tables that support BI and metrics layers.
- Coordinate change management and deprecation plans.
- Publish contracts and lineage to prevent regressions.
- Office hours and docs reduce ticket volume and delays.
3. Alignment with platform SRE
- Joint focus on reliability, security, and cost efficiency.
- Alignment reduces operational noise and risk.
- Define SLOs, budgets, and capacity planning cycles.
- Share dashboards, alerts, and runbooks for incidents.
- Integrate upgrades, patches, and cluster baselines.
- Post-incident actions drive systemic improvements.
When should a team use the hiring JD databricks to scale?
A team should use the hiring JD databricks to scale during platform migrations, product analytics rollouts, and AI delivery. Trigger requisitions at clear capacity thresholds.
1. Product analytics launch milestones
- Telemetry pipelines, LTV models, and experimentation frameworks.
- Dedicated capacity protects roadmap timelines and quality.
- Staff for ingestion, modeling, and freshness guarantees.
- Isolate responsibilities across features and domains.
- Track dependencies with a realistic onboarding plan.
- Tie objectives to launch dates and adoption metrics.
2. Hadoop-to-Lakehouse migration phases
- Legacy offload, reconciliation, and parity validation.
- Added capacity accelerates decommissioning and savings.
- Plan dual-runs with reconciliation dashboards and tests.
- Decide cutover gates tied to KPIs and error budgets.
- Publish playbooks for backfills and rollback steps.
- Lock in cost targets with resource and storage tuning.
3. GenAI and RAG productionization
- Vector pipelines, feature generation, and guardrails.
- Platform rigor prevents drift and leakage under scale.
- Build retrieval flows with governed embeddings and caches.
- Monitor relevance, cost, and safety metrics continuously.
- Coordinate with model teams on release cadences.
- Instrument feedback loops for iterative gains.
Open a requisition aligned to milestones and get shortlists quickly
Faqs
1. Which experience level fits this databricks engineer job description?
- Mid to senior engineers with 3–8 years in Spark production delivery; for lead scope, 8+ years with architecture ownership.
2. Does the template suit AWS, Azure, or GCP?
- Yes; adjust cloud-native services, IAM models, networking patterns, and storage conventions per provider.
3. Which certifications add value for candidates?
- Databricks Certified Data Engineer Associate or Professional, plus AWS/Azure/GCP data engineering credentials.
4. Can this JD cover both data and ML workloads?
- Yes; include MLflow, Feature Store, batch and streaming pipelines, and model deployment guardrails.
5. Which programming languages are essential?
- Python with PySpark and SQL as core skills; Scala experience is beneficial; bash and scripting familiarity helps.
6. Which KPIs validate impact in the first 90 days?
- SLA adherence, cost per job trend, data freshness, incident rate, deployment frequency, and lead time to restore.
7. Does the JD support remote or hybrid teams?
- Yes; clarify time-zone coverage, on-call expectations, secure access controls, and collaboration tooling.
8. Which interview task best evaluates real skills?
- A notebook exercise that tunes an inefficient Spark job and repairs a corrupted Delta table with an incident narrative.
