Data Platform Engineer

You’ll build, operate, and evolve the end-to-end data platform that powers analytics, automation, and AI use cases. This is a hands-on role spanning cloud infrastructure, ingestion/ETL, and data modeling across a Medallion (bronze/silver/gold) architecture. You’ll partner directly with stakeholders to turn messy source data into trusted datasets, metrics, and data products.

• Pragmatic Builder: You write clear SQL/Python, ship durable systems, and leave pipelines more reliable than you found them.
• Data-Savvy Generalist: You’re comfortable moving up and down the stack (cloud, pipelines, warehousing, and BI) and picking the right tool for the job.
• Fundamentals-first & Customer-Centric: You apply strong data modeling principles and optimize the analyst/stakeholder experience through consistent semantics and trustworthy reporting.
• Low-Ego, High-Ownership Teammate: You take responsibility for outcomes, seek feedback openly, and will roll up your sleeves to move work across the finish line.
• High-Energy Communicator: You’re comfortable presenting, facilitating discussions, and getting in front of stakeholders to drive clarity and alignment.
• Self-Starter: You unblock yourself, drive decisions, and follow through on commitments; you bring a strong work ethic and invest in continuous learning.

• Ingestion & ETL: Build reusable ingestion and ETL frameworks (Python and Spark) for APIs, databases, and un/semi-structured sources; handle JSON/Parquet and evolving schemas.
• Medallion Architecture: Own and evolve Medallion layers (bronze/silver/gold) for key domains with clear lineage, metadata, and ownership.
• Data Modeling & Marts: Design dimensional models and gold marts for core business metrics; ensure consistent grain and definitions.
• Analytics Enablement: Maintain semantic layers and partner on BI dashboards (Sigma or similar) so metrics are certified and self-serve.
• Reliability & Observability: Implement tests, freshness/volume monitoring, alerting, and runbooks; perform incident response and root-cause analysis (RCA) for data issues.
• Warehouse & Performance: Administer and tune the cloud data warehouse (Snowflake or similar): compute sizing, permissions, query performance, and cost controls.
• Standardization & Automation: Build paved-road patterns (templates, operators, CI checks) and automate repetitive tasks to boost developer productivity.
• AI Readiness: Prepare curated datasets for AI/ML/LLM use cases (feature sets, embeddings prep) with appropriate governance.

• 3–5+ years hands-on data engineering experience; strong SQL and Python; experience building data pipelines end-to-end in production.
• Strong cloud fundamentals (AWS preferred; other major clouds acceptable): object storage, IAM concepts, logging/monitoring, and managed compute.
• Experience building and operating production ETL pipelines with reliability basics: retries, backfills, idempotency, incremental processing patterns (e.g., SCDs, late-arriving data), and clear operational ownership (docs/runbooks).
• Solid understanding of Medallion / layered architecture concepts (bronze/silver/gold or equivalent) and experience working within each layer.
• Strong data modeling fundamentals (dimensional modeling/star schema): can define grain, build facts/dimensions, and support consistent metrics.
• Working experience in a modern cloud data warehouse (Snowflake or similar): can write performant SQL and understand core warehouse concepts.
• Hands-on dbt experience: building and maintaining models, writing core tests (freshness/uniqueness/RI), and contributing to documentation; ability to work in an established dbt project.
• Experience with analytics/BI tooling (Sigma, Looker, Tableau, etc.) and semantic layer concepts; ability to support stakeholders and troubleshoot issues end-to-end.

• Snowflake administration depth: warehouse sizing and cost management, advanced performance tuning, clustering strategies, and designing RBAC models
• Advanced governance & security patterns: masking policies, row-level security, and least-privilege frameworks as a primary implementer/owner
• Strong Spark/PySpark proficiency: deep tuning/optimization and large-scale transformations.
• dbt “platform-level” ownership: CI/CD-based deployments, environment/promotion workflows, advanced macros/packages, and leading large refactors or establishing standards from scratch.
• Orchestration: Airflow/MWAA DAG design patterns, backfill strategies at scale, dependency management, and operational hardening
• Sigma-specific depth: semantic layer/metrics layer architecture in Sigma, advanced dashboard standards, and organization-wide “certified metrics” rollout.
• Automation / iPaaS experience: Workato (or similar) for business integrations and operational workflows.
• Infrastructure-as-code: Terraform (or similar) for data/cloud infrastructure provisioning, environment management, and safe change rollout.
• Data observability & lineage tooling: OpenLineage/Monte Carlo-style patterns, automated lineage hooks, anomaly detection systems.
• Lakehouse / unstructured patterns: Parquet/Iceberg, event/data contracts, and advanced handling of semi/unstructured sources.
• AI/ML/LLM data workflows: feature stores, embeddings/RAG prep, and privacy-aware governance.

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