Data Engineer

Rippling, Inc.; Columbus (OH); On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Medical, Dental, Vision Insurance

Paid time off

Short- and Long-Term Disability

Opportunities to learn and grow

Job summary

AndHealth is building a modern data platform to power analytics and AI initiatives. You will own the ingestion layer and design scalable pipelines that deliver clean, trustworthy data into the warehouse while enforcing HIPAA security and data contracts.

You will champion reliability, observability, and cost-optimized BigQuery-based warehousing, with Airflow-driven orchestration and collaboration with analytics engineers across the company.

Qualifications

• 5+ years of hands-on data engineering experience building and operating production data systems.
• You’ve built ingestion systems from scratch handling unreliable sources, schema changes, and diverse formats.
• You design scalable, resilient data platforms with a strong emphasis on data contracts and quality.
• Proficient in Python ETL/ELT workflows and robust SQL for large-scale transformations.
• Deep understanding of data warehouse concepts (star schemas, slowly changing dimensions) and modeling.
• Experience with cloud data platforms (GCP/BigQuery), cost optimization, and partitioning/clustering.
• Ability to diagnose incidents, optimize performance, and implement reliable backfills.
• Mentor teammates, establish standards, and drive architectural patterns across teams.

Responsibilities

• Build and own the ingestion layer with scalable onboarding of data sources (file, API, streaming).
• Design production-grade, idempotent pipelines with recovery and backfill support.
• Develop data quality, observability, and alerting infrastructure at the platform level.
• Own orchestration, scheduling, and reliability with clear SLAs and recovery procedures.
• Manage warehouse layers, including performance tuning, partitioning, and access control in BigQuery.
• Translate diverse healthcare data into clean raw and staging datasets for analytics use.
• Create reusable ingestion and transformation frameworks for team-wide reuse.
• Integrate with healthcare data sources and ensure HIPAA-compliant security and data contracts.

Skills

Data modeling

ETL/ELT pipelines

Python programming

SQL advanced

Cloud data platforms

System design

Mentoring

HIPAA compliance

Education

Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or related field

Tools

Airflow

BigQuery

dbt

GCP

Job description

AndHealth is on a mission to radically improve access and outcomes for the most challenging chronic health conditions, with the goal of making world-class specialty care accessible and affordable to all. We partner with health systems, community health centers, and independent practices to remove barriers to care to ensure all people have access to the care they deserve.

About the Role

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.

What You'll Do:

• 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.

Education & Experience:

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related technical field preferred.

Other Skills & Qualifications:

Required

• 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.

Preferred

• 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.

Here’s what we’d like to offer you:

• Equal investment and support for our people and patients.
• A fun and ambitious start-up environment with a culture that takes on big things, takes risks, and learns quickly.
• The ability to demonstrate creativity, innovation, and conscientiousness, and find joy in working together.
• A team of highly skilled, incredibly kind, and welcoming employees, every one of whom has something unique to offer.
• We know that the overall success of our business is a collaborative effort, and we strive to provide ongoing opportunities for our employees to learn and grow, both personally and professionally.
• Full-time employees are eligible to participate in our benefits package which includes Medical, Dental, Vision Insurance, Company paid time off, Short- and Long-Term Disability, and more.

We are an equal opportunity and affirmative action employer. We embrace diversity and are committed to creating an inclusive environment for all employees. Applicants will be considered for employment without regard to race, religion, gender, gender identity, sexual orientation, national origin, age, disability, or veteran status.