Data Engineer

• Define end-to-end data architecture (lake/lakehouse/warehouse, batch/streaming, CDC, metadata).
• Set standards for schemas, contracts, orchestration, storage layers, and semantic/metrics models.
• Publish roadmaps, ADRs/RFCs, and “north star” target states; guide build vs. buy decisions.
• Design and build scalable, observable ELT/ETL and event pipelines.
• Establish ingestion patterns (CDC, file, API, message bus) and schema-evolution policies.
• Provide self-service tooling for analysts/scientists (dbt, notebooks, catalogs, feature stores).
• Ensure workflow reliability (idempotency, retries, backfills, SLAs).
• Define dataset SLAs/SLOs, freshness, lineage, and data certification tiers.
• Enforce contracts and validation tests; deploy anomaly detection and incident runbooks.
• Partner with governance on cataloging, PII handling, retention, and access policies.
• Lead capacity planning, partitioning/clustering, and query optimization.
• Introduce SRE-style practices for data (error budgets, postmortems).
• Drive FinOps for storage/compute; monitor and reduce cost per TB/query/job.
• Implement encryption, tokenization, and row/column-level security; manage secrets and audits.
• Align with SOC 2 and privacy regulations (e.g., GDPR/CCPA; HIPAA if applicable).
• Deliver versioned, documented datasets/features for BI and ML.
• Operationalize training/serving data flows, drift signals, and feature-store governance.
• Build and maintain the semantic layer and metrics consistency for experimentation/BI.
• Provide technical leadership across squads; mentor senior/staff engineers.
• Run design reviews and drive consensus on complex trade-offs.
• Translate business goals into data products with product/analytics leaders.

Principal Data Engineer

Senior Software Engineer – Data Engineering

Data Architect

Data Engineer

Senior Principal Data Architecture – Corporate Functions

Data Engineer