Deliver curated, reusable datasets for BI, analytics, and ML.
Build and run ingestion, transformation, and serving pipelines (batch and streaming).
Implement governance: ownership, PII handling, RBAC, retention, audit and compliance.
Set up and monitor data quality and observability (checks, alerts, incident handling).
Design and own scalable data platform architecture (lake/Lakehouse/warehouse).
Define and track dataset SLOs/SLAs (freshness, completeness, latency).
Define and enforce data standards: modeling, layering, naming, metadata, contracts.

6+ years in data architecture, data engineering, and DataOps (production environments)
Strong SQL experience
Strong Python experience
Experience building and maintaining production data pipelines
Experience designing and operating cloud data platforms (AWS and/or Azure or equivalent)
Experience with data lakes, Lakehouse, and/or data warehouses
Experience with data modeling and layering
Experience with data contracts and schema management
Experience with data governance (ownership, PII handling, RBAC, retention, auditability)
Experience with data quality controls and observability
Experience with metadata and data lineage
Experience with CI/CD practices for data systems
Experience with infrastructure as code (e.g., Terraform or equivalent)
Experience with batch and streaming data processing
Experience with at least one orchestration tool (Airflow, Dagster, Prefect, or similar)
Experience with at least one transformation tool (dbt or equivalent)
Experience with at least one streaming system (Kafka, Kinesis, Event Hubs, Flink, or Spark Streaming)
Experience with Lakehouse table formats (Delta, Iceberg, or Hudi)
Experience with catalog and governance tools (Purview, Collibra, DataHub, Unity Catalog, or Alation)
Experience with BI tools (Power BI, Tableau, or Looker)
Experience with data quality/observability tools (Great Expectations, Soda, or equivalent)
Familiarity with Kubernetes and containerized workloads

Senior Data Platform Engineer

Key skills