Data Engineer

AndHealth•Columbus, OH

2d•Onsite

About The Position

We are building the modern data platform that powers AndHealth’s analytics, reporting, operational workflows, product integrations, and AI initiatives. This is a senior, hands-on infrastructure and pipeline engineering role. You will own the systems that ingest, transport, transform, and serve data – the foundational layer that everything else at AndHealth depends on. Healthcare data is messy. Partner feeds arrive in inconsistent formats with no warning when schemas change. Source systems span decades of technical debt: flat files, HL7 feeds, proprietary exports, and undocumented APIs. Compliance requirements are strict and non-negotiable. We need someone who has been through this before and knows how to build ingestion and transformation systems that absorb that complexity, so that by the time data reaches the warehouse, it is clean, consistent, and trustworthy. We expect engineers to leverage AI tools thoughtfully to move faster, and to bring good judgment about where automation helps and where it introduces risk.

Requirements

5+ years of hands-on data engineering experience, with a track record of building and operating production data systems.
You’ve built ingestion systems from scratch. You’ve dealt with unreliable source systems, inconsistent file formats, undocumented APIs, and schema changes that arrive without warning. You know how to build frameworks that handle these problems systematically.
Strong infrastructure and platform thinking. You make build-vs-buy decisions, evaluate new tooling, and set the standards the rest of the team builds on. You’ve designed systems, not just contributed to them.
Production pipeline engineering with Python: building ETL/ELT workflows, handling file-based and API-based integrations, managing retries and error handling.
Advanced SQL skills: complex joins, CTEs, window functions, query optimization, and large-scale transformations. You should be able to look at a slow query and know where to start.
Strong understanding of data warehouse architecture and modeling patterns: star schemas, slowly changing dimensions, incremental models, and when to apply each.
Deep fluency with Cloud Data Platforms (preferably GCP and BigQuery), partitioning strategies, clustering, slot economics, materialization trade-offs, and cost optimization.
Design for resilience, not just correctness. You think about failure modes, backfill strategies, idempotency, and what happens when a source schema changes at 2 AM on a Friday.
Experience troubleshooting and resolving production incidents: diagnosing pipeline failures, data anomalies, and performance bottlenecks under pressure.
Deep experience with pipeline orchestration and reliability engineering. You’ve designed DAGs with complex dependency chains, built retry and backfill mechanisms, implemented SLA monitoring. You know the difference between a pipeline that works and a pipeline that’s production-ready.
Raise the bar for the team. You establish patterns, write reusable systems, define standards, and mentor junior engineers.

Nice To Haves

Experience working with healthcare data: EHR, claims, pharmacy, billing, or revenue cycle datasets. You understand the quirks—messy provider taxonomies, adjudication cycles, NDC codes, ICD/CPT mapping.
Familiarity with healthcare interoperability standards: HL7, FHIR, CCD, or EDI.
Experience implementing data quality, observability, governance, lineage, or metadata management solutions.
Experience working in a startup or high-growth technology environment.
We expect engineers to leverage AI tools thoughtfully to move faster, and to bring good judgment about where automation helps and where it introduces risk.

Responsibilities

Build and own the ingestion layer. Design scalable frameworks for onboarding new healthcare partner data sources: file-based, API-based, streaming, with standardized validation, error handling, and schema evolution support.
Design and maintain production-grade data pipelines that are idempotent, incremental where appropriate, and built to recover gracefully from failures.
Build the data quality and observability infrastructure. Implement schema validation, row-count reconciliation, freshness checks, anomaly detection, and alerting at the platform level.
Own orchestration, scheduling, and pipeline reliability. Every pipeline has clear SLAs, dependency management, failure alerting, and documented recovery procedures. You build the runbooks, the backfill tooling, and the incident response patterns.
Manage the warehouse infrastructure layer. Performance tuning, partitioning and clustering strategies, cost optimization, access control, and environment management in BigQuery.
Translate complex healthcare source systems into clean, standardized raw and staging datasets that analytics engineers and analysts can build on with confidence. This includes messy, semi-structured partner data from EHRs, claims systems, pharmacy platforms, and billing feeds.
Build reusable ingestion and transformation frameworks that the team can extend without reinventing the wheel. Think config-driven pipelines, shared libraries, and standardized patterns.
Manage integrations with healthcare partners and external data sources, including HRSA, CMS, Medicaid, FDA Orange Book, and federal drug pricing reference datasets. Own the ingestion contracts, handle schema drift, and ensure no data is silently lost or corrupted.
Ensure HIPAA-compliant security, privacy, and access controls throughout the data lifecycle, including PII detection and masking, role-based access, encryption, and audit logging.
Define and enforce data contracts between source systems and the data platform, and between the platform and downstream consumers. When something changes upstream, you know about it before it causes damage.