Data Engineer

• Define enterprise data blueprints including conceptual, logical, and physical data models.
• Establish canonical data models and a unified semantic layer across HRIS, Finance, Sales, and Operations data.
• Standardize data integration practices (batch, micro-batch, and streaming) using modern ETL/ELT frameworks.
• Develop architecture patterns for data lakes, data warehouses, and data lakehouse environments (e.g., Databricks, Snowflake, Synapse, BigQuery, Redshift).
• Ensure data designs are aligned with performance, scalability, and cost optimization across multi-cloud environments.
• Apply Kimball and Inmon methodologies for dimensional and denormalized modeling, selecting appropriately per use case.
• Drive data product thinking: domain-oriented ownership, versioned schema, backward compatibility, discoverability, and consumer contracts.
• Implement Agile DataOps and CI/CD pipelines for data model deployment and integration.
• Promote engineering fundamentals for data services (API driven patterns, resiliency, 12-factor as a guideline) and adopt architecture frameworks (e.g., TOGAF/Zachman) as appropriate for consistency.
• Ensure architecture supports event-driven design and streaming-first principles where appropriate.
• Champion open data formats/engines (Iceberg/Delta/Hudi; Dremio/Trino/Presto) to avoid lock-in and enable hybrid/on prem.
• Implement CDC and streaming patterns (Debezium + Schema Registry; Kafka/Kinesis/Pub/Sub) with materialized views.
• Work with leaders and ensure alignment to enterprise architecture.
• Partner with the Data Engineering Leader on pipeline architecture, tooling, and performance tuning.
• Partner with the Data Governance Leader on master data management (MDM), lineage, and cataloging standards.
• Directly lead a small team of data architects; set goals, coach growth, and run architecture reviews/ADRs and act as technical advisor to the VP of Enterprise Intelligence and other executives on data strategy.
• Champion metadata-first architecture, ensuring lineage and cataloging are embedded in all processes.
• Align with governance frameworks for RBAC/ABAC, PII/PHI handling, encryption (AES-256, TLS 1.3), and tokenization.
• Ensure compliance with GDPR, HIPAA, SOC2, CCPA and client-specific contractual obligations.
• Enable AI/ML and LLM Workloads: RAG patterns, vector search (pgvectore/FAISS/Weaviate/Pinecone), un/structured pipelines, prompt/response logging, and evalution telemetry.

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