Data Pipeline & Ingestion Engineer; Senior​/Mid

Full-time

5+ years

US

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

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