Senior Data Engineer

Job Title: LEAD Data Engineer Location: Overland Park, KS & Frisco, TX (Hybrid) Duration: Long Term Contract Mandatory Skills: Azure | Databricks | Snowflake, Software Engineering, Data Modeling, Databricks, Snowflake, Spark, Data Pipelines, Azure data factory Azure-native third-party data enrichment platform using Databricks/Spark + Snowflake; focus on reliable governed pipelines, strong Spark troubleshooting, privacy/governance, and cost-aware engineering; Team / Business Context: You will join a data engineering team responsible for third party data enrichment, augmenting first party datasets with external identity/attribute data to support analytics, activation, and research. The enriched datasets are consumed by multiple downstream systems and teams, including the Customer Data Platform (CDP) and other analytics/research stakeholders. The platform is Azure-native and built primarily on Databricks (processing + some ML workloads) and Snowflake (analytics/warehouse). A major focus is building reliable, governed, vendor agnostic datasets while ensuring privacy/compliance, data governance, and cost efficiency. Key Responsibilities: As a Data Engineer, you will: Data Ingestion & Pipeline Development Build and enhance ingestion pipelines for large batch and event-driven paths (streaming may evolve over time). Integrate data from: Third party enrichment vendors (identity + attributes, very large volumes) Digital platforms via Conversion API (CAPI) integrations (through intermediary/middleware) Rewards/Promotions systems (e.g., TMT) for offer issuance/redemption/consumption data Data Quality, Reliability & Operations Implement strong data validation, idempotency, replay/backfill strategies, and deduplication to prevent quality drift. Own monitoring, alerting, dashboarding, and operational readiness ( wrappers around core pipelines). Troubleshoot failures with root cause analysis not just reruns: Interpret Spark logs Diagnose performance issues (shuffle, skew, partitioning) Improve stability and SLA adherence Governance & Compliance (First-class NFR) Apply privacy, compliance, and governance requirements across pipelines and datasets. Support governance standards such as: Unity Catalog, lineage, access controls Managing PII vs non PII access Documentation of tables, schemas, catalogs, and cluster usage Cost Governance & Performance Optimization Design pipelines with cost awareness from day one: Cluster sizing, workload tuning, efficient compute/storage usage Trade-off decisions balancing cost vs quality vs SLA Collaboration & Ownership Work in a small, fast-moving team; be self-driven and ownership-oriented. Raise and manage data quality escalations when issues are detected. Contribute to evolving architecture (product is early-stage; first live month was recent). Must-Have Skills: Candidate with hands-on, recent experience in: Strong coding: PySpark + SQL (hands-on, not only orchestration) Databricks: notebooks/jobs, performance tuning fundamentals, medallion patterns Spark fundamentals: partitioning, skew/shuffle optimization, understanding failures via logs Snowflake: data modeling/usage for analytics/warehousing workloads Azure ecosystem: Azure Data Factory (ADF) (orchestration) Azure-native integrations and services exposure Data engineering reliability patterns: validation, idempotency, replay/backfills, dedup, auditability Data governance: Unity Catalog (preferred), lineage, access control patterns, PII handling Ownership mindset: can execute independently without constant approvals/check-ins Nice-to-Have Skills: Event-driven/streaming ingestion exposure (even if primary is batch today) Delta/Databricks patterns such as Delta Live Tables (DLT) (some workflows exist) Experience building config-driven export frameworks for multiple downstream consumers/vendors Exposure/interest in identity resolution concepts (ML optional; ETL strength is priority) Familiarity with CAPI integrations / marketing tech data signals Experience implementing operational telemetry: dashboards, alerts, SLA monitoring What Good Looks Like (Success Criteria) Ships reliable, well-governed datasets with strong data quality practices Can scale pipelines for very large volumes (hundreds of millions of records per vendor) Prevents silent failures where quality degrades without obvious job failures Balances delivery speed with compliance, governance, and cost controls