Data Engineer

Senior Data Engineer

Job family Data Engineering

Recommended level Senior Data Engineer Lead Data Engineer

Position summary

The Senior Data Engineer will build trusted real-time and historical data products for the

Real Time Finance Close and Value-Based Care platform

The role will integrate claims encounters member eligibility provider contract capitation

sub-capitation actuarial journal subledger GL payment and operational data The engineer

will create streaming and batch pipelines that support financial processing, actuarial analysis,

reconciliation Cube.dev semantic models Power BI reporting, ontology integration, and AI-

driven analytics

Support event streaming, APIs, change-data capture, files, databases, and partner

interfaces.

Develop reusable patterns for schema evolution, quarantine, reprocessing, and source-

system reconciliation.

Streaming and transformation engineering

Develop stateful and stateless streaming pipelines.

Implement effective-dated and bitemporal models.

Build automated controls for completeness, validity, uniqueness, timeliness

consistency, and referential integrity

Detect missing, duplicate, delayed, and materially changed records

Capture source-to-target lineage and business transformation lineage

Provide point-in-time reproducibility for accounting, actuarial, audit, and regulatory

review

Work with data stewards to define data-quality thresholds, issue ownership, and

Optimize data layouts and aggregations for predictable performance

Data platform operations

Build data pipelines through version-controlled, automated deployment processes

Monitor freshness, volume, schema, lineage, quality, and processing latency

Participate in production support, root-cause analysis, replay, and recovery

Required qualifications

Typically 5 or more years of data engineering experience

Experience with Kafka or another event-streaming platform

Experience building both batch and streaming data pipelines

Preferred qualifications

Experience with temporal, effective-dated, or bitemporal data models

Familiarity with X12 837/835, HL7, FHIR, or healthcare reference data

Experience with dbt or an equivalent transformation framework

Experience with Cube.dev or another governed semantic layer

Finance and Actuarial can reproduce results for a specific reporting date

Production pipelines recover safely and predictably after failure