Data Pipeline & Ingestion Engineer (Senior / Mid)
Job Type
Full-time
Experience
5+ years
Location
US
Job Description
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.
Key Responsibilities
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
Qualifications
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)
Bonus Points
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