Technology

Snowflake Engineer Job Description (Ready-to-Use Template)

|Posted by Hitul Mistry / 08 Jan 26

Snowflake Engineer Job Description

Which responsibilities define a Snowflake Engineer role?

Responsibilities that define a Snowflake Engineer role include pipeline design, SQL optimization, security governance, and cost management in Snowflake.

1. Data Pipeline Design

Batch and streaming ingestion into Snowflake using ELT patterns across cloud storage and connectors.
Schema evolution, incremental loading, and dependency mapping for reliable, scalable flows.
Business-ready tables through standardized modeling layers and tested transformations.
Lower lead time for analytics, enabling faster decisions and product iteration.
Staged ingestion with validation gates and idempotent loads through orchestration.
CDC patterns, file-based staging, and task-based scheduling coordinated with alerts.

2. SQL and Performance Tuning

Warehouse sizing, clustering keys, materialized views, and result cache leverage.
Query profile interpretation to remove hotspots, skews, and unnecessary scans.
Faster analytics with stable latencies and predictable SLAs across workloads.
Credits preserved through efficient joins, pruning, and micro-partition alignment.
Iterative tuning guided by query history, auto-clustering, and statistics review.
Rewriting patterns, partition-aware filters, and right-sized compute classes.

3. Security and Governance

Role-based access control, masking policies, and row access policies.
Data sharing setups, network policies, key management, and audit trails.
Compliance alignment for privacy, finance, and regulated workloads.
Reduced exposure risk with least privilege and strong lineage visibility.
Centralized roles, schema-level controls, and reproducible policy as code.
Continuous monitoring with alerts on privilege drift and anomalous access.

4. Cost and Resource Optimization

Warehouse lifecycle policies, auto-suspend, and multi-cluster strategies.
Storage hygiene, retention settings, and time travel tuning by domain.
Budget adherence and transparency for finance and product stakeholders.
Greater throughput per credit and clear unit economics per workload.
Credit dashboards, tagging, and workload isolation by environment and team.
Right-size experiments, off-peak scheduling, and archival tiering practices.

Map your snowflake engineer roles responsibilities to business outcomes with a quick review

Which skills are required in a Snowflake Engineer job description?

Skills required in a Snowflake Engineer job description span advanced SQL, ELT tooling, cloud platforms, programming, and CI/CD practices.

1. Advanced SQL and Snowflake SQL

Window functions, semi-structured data with VARIANT, and UDFs procedures.
Time Travel, Streams, Tasks, and Search Optimization for complex needs.
Precise analytics, lower latency, and robust transformations at scale.
Reliable delivery of metrics and features for downstream consumers.
Query plans analyzed with profile tools and rewritten for minimal scans.
Feature flags and controlled rollouts supported with metadata design.

2. ELT with dbt and Orchestration

dbt for modular models, tests, and documentation with version control.
Airflow or cloud schedulers for dependency-aware runs and retries.
Reusable logic and transparent lineage for governed analytics.
Faster onboarding and safer change management across teams.
CI on pull requests, data tests, and deployment pipelines in Git.
Incremental builds and seed management aligned to environments.

3. Cloud Platform Proficiency (AWS, Azure, GCP)

Storage layers, IAM concepts, networking, and secret management.
Event services, serverless, and monitoring stacks integrated with Snowflake.
Secure, scalable data movement aligned to enterprise standards.
Consistent controls across multi-cloud or hybrid footprints.
Cross-account roles, private links, and bucket policies configured.
Observability wired with logs, metrics, and traces for stack health.

4. Python and Automation

Data utilities, validation scripts, and SDK-driven administration.
Packaging, virtual environments, and code style consistency.
Reduced toil through scripted operations and repeatable tasks.
Faster iterations on experiments and diagnostics for teams.
Automation via APIs, Terraform, and CI runners with approvals.
Notebook workflows and service containers for reproducibility.

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Which ready-to-use Snowflake Engineer job description template can be copied?

The ready-to-use template below outlines role summary, responsibilities, qualifications, and hiring criteria for a Snowflake Engineer.

1. Role Summary

Title: Snowflake Engineer; Type: Full-time; Location: Hybrid/Remote; Team: Data.
Mission: Build secure, performant, and cost-efficient analytics on Snowflake.
Business impact through trusted data, faster insights, and scalable platforms.
Clear ownership across ingestion, modeling, reliability, and governance.
Deliver curated datasets, maintain SLAs, and optimize credits and storage.
Collaborate with data scientists, analysts, and product on priorities.

2. Key Responsibilities

Design ELT pipelines, model layers, and semantic structures in Snowflake.
Tune queries, warehouses, and storage for performance and spend control.
Enable decision velocity, product analytics, and ML feature readiness.
Drive compliance alignment and resilient, observable data services.
Manage RBAC, masking, auditing, and secure sharing configurations.
Establish monitoring, incident response, and capacity planning rhythms.

3. Qualifications

3–8 years in data engineering with cloud data platforms experience.
Proficiency in SQL, dbt, Python, and a major cloud provider.
Readiness for enterprise scale, security, and regulated contexts.
Proven delivery of reliable pipelines and analytics-ready datasets.
Degree in CS, Engineering, or equivalent practical experience.
SnowPro or cloud associate certifications preferred.

4. Skills and Tools

Snowflake features: Streams, Tasks, Time Travel, policies, shares.
Ecosystem: dbt, Airflow, Fivetran, Terraform, GitHub Actions.
Strong alignment to governance, performance, and unit economics.
Effective collaboration with product, analytics, and security.
Observability via query history, logs, lineage, and quality checks.
BI familiarity: Tableau, Power BI, or Looker for downstream needs.

5. Experience Expectations

Delivered cross-domain ingestion, transformation, and modeling.
Operated production data services with SLAs and on-call support.
Greater reliability and cost efficiency sustained over quarters.
Demonstrable metrics improvements and stakeholder satisfaction.
Migration or modernization projects across legacy to Snowflake.
Documentation, runbooks, and knowledge transfer across teams.

6. Success Metrics

P95 query latency, credits per query, and freshness objective rates.
Incident rate, MTTR, change failure rate, and deployment frequency.
Transparent accountability to data consumers and leadership.
Continuous gains in platform efficiency and reliability targets.
Adoption of curated datasets and reduction in ad-hoc pipelines.
Audit pass rates and policy conformance across environments.

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Which outcomes should a Snowflake Engineer deliver in the first 90 days?

Outcomes in the first 90 days should cover environment readiness, baseline pipelines, performance benchmarks, and governance foundations.

1. Environment Readiness

Accounts, roles, warehouses, databases, and schema baselines in place.
Access controls, secrets, and network policies standardized.
Secure foundation that accelerates subsequent delivery streams.
Lower risk of drift through policy and code-based definitions.
IaC templates, runbooks, and golden path examples shared.
Observability wired from day one for rapid feedback loops.

2. Ingestion and Modeling Milestones

Priority sources landed with incremental and CDC patterns.
Core models built with dbt tests and documentation.
Business questions answered faster with trusted datasets.
Fewer ad-hoc extracts through governed, reusable layers.
Data contracts agreed with producers and enforced.
CI pipelines validating schemas and transformations on commit.

3. Performance Baselines

Benchmarks for key workloads and query classes captured.
Warehouse sizing standards and auto-suspend policies set.
Predictable latency and throughput for priority dashboards.
Reduced spend variance through tuned compute policies.
Query review cadence and regression alerts instituted.
Clustering and pruning strategies adopted for major tables.

4. Governance Foundations

RBAC map, masking, and row policies aligned to domains.
Data sharing guidelines and audit logging activated.
Clear stewardship and accountability for sensitive data.
Fewer access exceptions through standardized patterns.
Data catalog entries and lineage coverage established.
Periodic reviews, attestation, and remediation workflows.

Plan a 90-day roadmap aligned to your snowflake engineer job description

Which interview screening criteria validate Snowflake Engineer capability?

Interview screening criteria should validate SQL depth, platform fluency, architecture sense, and operational ownership.

1. Technical Screening

Problem-solving with window functions, semi-structured data, and joins.
Diagnostic reads of query profiles and clustering impacts.
Evidence of rigorous reasoning and safe optimization choices.
Fit for complex domains with changing data and stakeholders.
Timed exercises with realistic datasets and constraints.
Verbal walkthroughs of trade-offs and alternative paths.

2. Practical Exercise

Build a dbt model set with tests and documentation.
Configure a warehouse strategy for diverse workloads.
Observable outputs that mirror production standards.
Confidence in reproducibility and maintainability goals.
Git-based PR, CI checks, and deployment notes delivered.
Cost and performance notes attached to design decisions.

3. Architecture Review

Design for ingestion, modeling, governance, and observability.
Isolation strategy across environments, teams, and SLAs.
Alignment to scalability, resilience, and compliance needs.
Clear ownership boundaries and interfaces across teams.
Diagrams, assumptions, and risk registers presented.
Phased rollout and migration approach with guardrails.

4. Collaboration and Ownership

Cross-team alignment with analytics, security, and product.
Incident response habits, retrospectives, and RCA culture.
Smoother delivery and lower defects through shared rituals.
Shared vocabulary and expectations across stakeholders.
Ticket hygiene, documentation, and communication samples.
Examples of mentoring, pair sessions, and enablement.

Use a calibrated rubric to screen Snowflake engineers with confidence

Which metrics track success for a Snowflake Engineer?

Metrics that track success include performance, cost efficiency, reliability, and data quality indicators.

1. Query Performance KPIs

Median and P95 latency by workload, plus concurrency trends.
Cache hit rates, scan volumes, and clustering effectiveness.
Faster user journeys and higher dashboard satisfaction.
Improved throughput enabling more experiments per cycle.
Scheduled performance reports and regression alerts.
Playbooks for hotspots and emergency tuning paths.

2. Cost Efficiency KPIs

Credits per query, warehouse, and domain over time.
Storage growth, retention, and archival ratios tracked.
Budget predictability with clear unit cost baselines.
Higher utilization without contention or saturation.
Cost dashboards, tags, and chargeback models adopted.
Right-sizing and off-peak scheduling tactics enforced.

3. Reliability and SLAs

Freshness SLOs, delivery windows, and on-time rates.
Incident counts, MTTR, change failure, and rollbacks.
Trust in data flows and steady stakeholder confidence.
Lower firefighting with sustainable operating rhythms.
Error budgets, runbooks, and escalation paths defined.
Synthetic checks, canaries, and recovery drills run.

4. Data Quality and Governance KPIs

Test pass rates, anomaly flags, and drift detection.
Policy conformance, access reviews, and audit results.
Confidence in analytical outputs and ML features.
Reduced rework from upstream defects and breaks.
Data contracts coverage and SLA enforcement levels.
Remediation SLAs and aging of defects monitored.

Benchmark your KPIs against peers and refine targets

Which variations fit a Snowflake developer JD template by seniority?

Variations by seniority align scope, autonomy, and architectural depth across levels.

1. Junior Snowflake Engineer

Focus on well-scoped tickets across ingestion and modeling.
Exposure to performance reviews and governance basics.
Rapid skill growth with measurable delivery contributions.
Low-risk tasks that build confidence and consistency.
Pairing sessions, templates, and documented playbooks.
Structured feedback loops and curated learning paths.

2. Mid-level Snowflake Engineer

Ownership of pipelines, models, and service reliability.
Regular tuning, cost reviews, and policy updates.
Clear outcomes with stable SLAs and stakeholder trust.
Demonstrated improvements across latency and spend.
Domain stewardship with cross-team coordination.
Backlog shaping and technical decision proposals.

3. Senior Snowflake Engineer

End-to-end design for complex domains and workloads.
Standards across performance, security, and operations.
Organization-wide impact through reusable patterns.
Visible gains in resilience, velocity, and unit costs.
Mentorship, reviews, and architecture facilitation.
Roadmaps aligned to product and compliance goals.

4. Lead or Architect Snowflake Engineer

Multi-domain architecture, capacity, and governance.
Portfolio-level cost, reliability, and risk posture.
Strategy alignment to business platforms and outcomes.
Compounded efficiency through platform investments.
Talent development, hiring, and vendor partnership inputs.
KPIs tied to enterprise transformation objectives.

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Which tips improve a hiring JD snowflake for market-ready talent?

Tips that improve a hiring JD snowflake center on outcomes, stack clarity, process transparency, and inclusive language.

1. Outcomes-Focused Language

Emphasize impact, KPIs, and 90-day objectives.
Tie duties to reliability, performance, and cost.
Signals aligned incentives and clarity of success.
Appeals to builders focused on measurable results.
Include latency, credits, freshness, and SLO targets.
Reference supported domains and real use cases.

2. Tech Stack Specificity

List Snowflake features, dbt, orchestration, and IaC.
Note cloud provider services and BI consumers.
Reduces mismatch and speeds cycle time to hire.
Attracts candidates with relevant, recent experience.
Name versions, tools, and integrations in use.
Map responsibilities to the exact toolchain.

3. Screening Process Clarity

Share stages, timelines, and evaluation criteria.
Provide realistic exercises and prep guidance.
Builds trust and raises completion rates for candidates.
Minimizes surprises and reduces interview noise.
Outline time commitment and formats for each stage.
Share scoring rubrics and pass thresholds.

4. Inclusive and Equitable Language

Avoid loaded terms and unnecessary degree filters.
Emphasize transferable skills and potential.
Larger, more diverse pipeline with stronger fit.
Fair access to opportunities across backgrounds.
Salary bands, benefits, and flexibility stated.
Accessible language with clear accommodations.

Attract market-ready talent with a refined snowflake engineer job description

Faqs

1. Which core skills belong in a snowflake engineer job description?

Advanced SQL, Snowflake features, ELT with dbt/Airflow, cloud platform fluency, Python, CI/CD, data modeling, and governance.

2. Which responsibilities should a Snowflake Engineer own day to day?

Pipeline development, schema design, query tuning, role-based security, cost control, monitoring, and incident response.

3. Which certifications strengthen a snowflake developer JD template?

SnowPro Core, SnowPro Advanced (Architect or Data Engineer), AWS/Azure/GCP associate-level, and dbt Fundamentals.

4. Which metrics evaluate success for a Snowflake Engineer?

Query latency percentiles, credits per workload, data freshness SLAs, reliability SLOs, and data quality conformance rates.

5. Which experience level fits startups vs enterprises in a hiring JD snowflake?

Startups benefit from generalists with broad stack coverage; enterprises benefit from specialists and architects for scale.

6. Which tools complement Snowflake in modern data stacks?

dbt, Airflow, Fivetran, Matillion, Kafka, Terraform, GitHub Actions, Great Expectations, Monte Carlo, and Tableau/Power BI.

7. Which interview tasks validate practical Snowflake skills?

SQL challenge, dbt model build, warehouse sizing exercise, role hierarchy design, and cost diagnosis from query history.

8. Which mistakes to avoid in a snowflake engineer job description?

Vague duties, outdated tech lists, missing outcomes and metrics, ignoring security and cost ownership, and unclear seniority.