What does a Data Platform Engineer do?
A Data Platform Engineer builds and maintains the shared data foundation that product, analytics, ML, and BI teams rely on. You design the architecture, provision scalable compute and storage, standardize ingestion and orchestration, enforce governance and security, and keep the platform observable, cost-efficient, and reliable.
- Own core components: data lake/warehouse, orchestration, streaming, catalog, quality checks, access controls.
- Enable others: templates, developer tooling, CI/CD for data, platform documentation, self-serve patterns.
- Ensure reliability: monitoring, SLAs/SLOs, incident response, cost optimization, capacity planning.
- Partner widely: security, infra/SRE, data engineers, analysts, ML engineers, stakeholders.
A day in the life (example)
- Morning: Review overnight pipelines, check data quality dashboards, triage alerts, merge a platform module update.
- Midday: Design session for a new streaming source; align on schemas, retention, access policies.
- Afternoon: Roll out a new Airflow DAG template, update RBAC for a new team, run a cost analysis on cold storage.
Typical deliverables
- Reference architecture diagrams, IaC modules, and environment blueprints.
- Production-ready ingestion templates (batch and streaming) and orchestration patterns.
- Data catalog with governed domains, lineage, and access policies.
- Quality checks, SLAs/SLOs, and on-call runbooks with dashboards and alerts.
- Developer experience components: local dev containers, CI/CD workflows, starter repos.
Who this is for
- Engineers who enjoy platform thinking, reliability, and enabling many data teams.
- Data engineers ready to scale from single pipelines to organization-wide platforms.
- SRE/infra engineers who want to specialize in data systems and governance.
- Builders who care about usability, security, and long-term maintainability.
Prerequisites
- Solid SQL and comfort with at least one programming language (Python or JVM-based).
- Basic Linux, containers, and CI/CD concepts.
- Familiarity with cloud concepts (compute, storage, networking) or willingness to learn.
- Comfort reading architecture diagrams and reasoning about trade-offs.
Hiring expectations by level
Junior
- Builds small features in the platform under guidance. Can operate pipelines and follow runbooks.
- Understands SQL, basic cloud storage/compute, and templated orchestration tasks.
- Salary (USD): ~70k–110k. Varies by country/company; treat as rough ranges.
Mid-level
- Owns a platform component end-to-end (e.g., orchestration templates or data catalog onboarding).
- Designs for scale, adds observability, enforces access patterns, and improves developer experience.
- Salary (USD): ~110k–160k. Varies by country/company; treat as rough ranges.
Senior/Staff
- Leads architecture across domains (batch + streaming), sets standards, mentors, and drives reliability and cost efficiency.
- Partners with security, SRE, finance, and leadership; plans capacity and roadmap.
- Salary (USD): ~150k–220k+ depending on scope and location. Varies by country/company; treat as rough ranges.
Where you can work
- Industries: fintech, e-commerce, SaaS, health, gaming, logistics, ad-tech, telecom, public sector.
- Teams: central data platform, data engineering, data SRE, cloud platform,
- Company sizes: startups (1–3 engineers wearing many hats) to enterprises (platform orgs with domain pods).
Skill map
Focus on foundations that make the platform scalable, secure, and easy to use:
- Data Platform Architecture: patterns for lakehouse/warehouse, domains, and interfaces.
- Infrastructure as Code: reproducible, versioned cloud resources and permissions.
- Compute and Storage Foundations: object storage, warehouses, clusters, and cost controls.
- Orchestration and Scheduling Platform: DAG design, retries, backfills, and SLAs.
- Streaming Platform Basics: topics, partitions, retention, and exactly-once patterns.
- Data Access and Security: RBAC/ABAC, secrets, network boundaries, and auditability.
- Data Catalog and Governance: domains, ownership, lineage, glossaries, policies.
- Data Quality and Observability: tests, anomaly detection, SLOs, and incident playbooks.
- Developer Experience for Data: starter templates, local dev, CI/CD, docs, and paved paths.
- Warehouse and Query Performance: partitioning, clustering, caching, query plans, and costs.
Learning path
Step 1: Sketch a platform architecture for a small company (batch first). Identify data domains, storage layers, and orchestration.
Step 2: Provision core storage and compute with Infrastructure as Code. Add basic RBAC and environment isolation.
Step 3: Create a DAG template and a streaming template. Include retries, alerts, backfills, and schema evolution.
Step 4: Add catalog, data quality checks, lineage, and dashboards for SLIs/SLOs. Publish runbooks.
Step 5: Optimize warehouse queries and storage layout. Validate cost/performance trade-offs.
Mini task: Define SLIs/SLOs
Pick two SLIs (e.g., pipeline success rate, data freshness). Propose SLO targets and alert thresholds. Add them to an on-call runbook.
Mini task: Access policy
Write an RBAC policy for a new analytics team: who can read which domains, who can write, and how secrets are rotated.
Practical projects
- Lakehouse Starter: Object storage + warehouse with IaC, domain folders, and lifecycle policies. Outcome: Reprovisionable data foundation with clear ownership.
- Batch + Orchestration: A daily ingestion and transformation DAG with backfills, SLAs, and alerting. Outcome: Reliable, observable pipeline template.
- Streaming MVP: Ingest a small real-time feed with schemas, compaction, and deduplication. Outcome: Low-latency dataset with replay and retention.
- Catalog & Governance: Register datasets, set ownership, PII tags, and lineage. Outcome: Discoverable, governed data assets.
- Quality & Observability: Great Expectations/SQL checks + dashboards for freshness and completeness. Outcome: Measurable quality with SLOs and runbooks.
- DX Toolkit: Cookiecutter starter repo, local dev containers, and CI/CD for data. Outcome: Faster onboarding and fewer platform tickets.
Interview preparation checklist
- Architecture: Explain trade-offs of lake vs warehouse vs lakehouse, batch vs streaming, and multi-zone/multi-region.
- IaC & Environments: Show how you structure modules, handle secrets, and promote changes safely.
- Reliability: Walk through SLIs/SLOs, incident response, and rollback/forward strategies.
- Security & Governance: RBAC/ABAC, data masking, PII handling, lineage, and audit trails.
- Performance & Cost: Partitioning, clustering, indexing, caching, file sizes, and query plans.
- DX & Standards: Templates, code reviews, documentation, and paved paths for teams.
- Hands-on: Write SQL to diagnose a slow query; sketch a DAG; propose a streaming retention plan.
Common mistakes and how to avoid them
- Building for ideals, not users: Involve end users early; ship paved paths and docs before advanced features.
- Skipping observability: Define SLIs/SLOs and alerts from day one; practice incident drills.
- Uncontrolled data growth: Apply lifecycle policies, compaction, and archiving; review costs monthly.
- Weak schema governance: Enforce contracts, versioning, and backward compatibility.
- Permission sprawl: Centralize RBAC, automate reviews, and log access; avoid one-off overrides.
- One-size-fits-all orchestration: Provide patterns for batch and streaming; document backfills and late data handling.
Exam on this page
The exam is open to everyone. If you log in, your progress and results are saved. Use it to validate readiness or find gaps.
Next steps
Pick a skill to start in the Skills section below. Build one project, set clear SLIs/SLOs, and iterate. Momentum beats perfect plans.