Data Engineer (Azure | Databricks | Snowflake) C2C jobs Overland Park, KS

Role Data Engineer (Azure | Databricks | Snowflake) Location: Overland Park, KS (Onsite/Hybrid) Key Responsibilities Build and enhance ingestion pipelines for large batch and event‑driven paths, integrating data from third‑party enrichment vendors, Digital platforms via Conversion API, Rewards/Promotions systems, and other sources. Ensure data quality, reliability & operations by implementing validation, idempotency, replay/backfill strategies and deduplication to prevent quality drift. Own monitoring, alerting, dashboarding and operational readiness, including wrappers around core pipelines. Troubleshoot failures with root‑cause analysis, interpreting Spark logs, diagnosing performance issues (shuffle, skew, partitioning) and improving stability and SLA adherence. Apply privacy, compliance and governance requirements across pipelines and datasets, supporting standards such as Unity Catalog, lineage, access controls, and PII handling. Design pipelines with cost awareness from day one: cluster sizing, workload tuning, efficient compute/storage usage and balancing cost vs quality vs SLA. Work collaboratively in a small, fast‑moving team, self‑driven and ownership‑oriented, raising and managing data quality escalations when issues are detected. Required Skills Strong coding in PySpark and SQL with hands‑on experience. Databricks: notebooks, jobs, performance tuning fundamentals, medallion patterns; Spark fundamentals (partitioning, skew/shuffle optimization, log analysis). Snowflake: data modeling and usage for analytics and warehousing workloads. Azure ecosystem: Azure Data Factory orchestration and Azure‑native integrations. Data engineering reliability patterns: validation, idempotency, replay/backfills, deduplication, auditability. Data governance: Unity Catalog (preferred), lineage, access control patterns, PII handling. Ownership mindset: ability to execute independently without constant approvals or check‑ins. Nice‑to‑Have Skills Event‑driven/streaming ingestion exposure, including Delta Live Tables (DLT). Experience building config‑driven export frameworks for downstream consumers/vendors. Interest in identity resolution concepts. Operational telemetry: dashboards, alerts, SLA monitoring. Success Criteria Ships reliable, well‑governed datasets with strong data quality practices; scales pipelines for very large volumes; prevents silent failures where quality degrades; balances delivery speed with compliance, governance, and cost controls. #J-18808-Ljbffr Tech Mirrors