Data Pipeline & Ingestion Engineer (Senior / Mid)

You will build and operate the data backbone of ODL: bulk and streaming ingestion from legacy source systems, medallion-layered storage (Bronze/Silver/Gold), identity resolution and golden-record consolidation, source-to-canonical mapping and crosswalks, and the data-quality and reconciliation gates that prove data is complete and correct before it is published. This is the volume engine of the program - every new client onboarded flows through the pipelines you build., * Build batch-seed and event-tail ingestion per source system, including seed tail watermark hand-off,idempotent upserts, and dedup ledgers

• Build and operate medallion layers with reprocess-from-Bronze, pipeline orchestration (checkpoints,retry/backoff, DLQ), and full observability
• Build data-quality gates (quarantine / pass-with-flag), quality scoring, and a reconciliation engine covering count, record, and financial reconciliation - financial is zero-tolerance
• Build identity matching combining deterministic rules with probabilistic scoring and confidence bands;deliver deduplication, golden-record materialization, and survivorship rules, calibrating match thresholds with labelled data
• Author and maintain source canonical structural mappings and value crosswalks (e.g., collapsing 1,800+ raw employment-status values to ~20 standard ones) as governed, versioned configuration
• Enforce data contracts at the boundary: schema registry, fail-fast validation, and semver-compatible schema evolution, * Probabilistic record linkage at depth - blocking/candidate generation, scoring models, threshold calibration (expected at senior level)
• Schema registry experience (Avro/Protobuf)
• Extracting from mainframe or older RDBMS sources with limited CDC support
• Financial reconciliation in finance-adjacent domains
• Benefits administration or healthcare domain knowledge

• 5+ years building production data pipelines at scale
• Kafka depth: consumers/producers, replay, DLQ, exactly-once / idempotent processing patterns
• Strong SQL and solid ETL fundamentals
• Java and/or Python in production
• Medallion / lakehouse layering, CDC, watermark/checkpoint patterns, and batch-stream hand-off
• Data-quality frameworks: validation rules, quarantine and re-entry, quality scoring, reconciliation
• Entity resolution / MDM exposure: record matching, dedup, survivorship - via commercial tools(Informatica MDM, Reltio) or custom builds
• Data mapping and crosswalk discipline: profiling messy datasets, authoring governed reference data,config-as-code (YAML/JSON, Git)