Data Engineer

ABOUT THE ROLE

We are looking for a Data Engineer to own the full breadth of our data platform; from the PostgreSQL schemas that power our core application, to the Kafka and SQS event streams that flow between services, to the analytical data warehouse that surfaces insights for clinical, commercial, and executive stakeholders. This is a platform ownership role, not just a pipeline maintenance role.

You will work in close partnership with our software engineering team to design data systems that serve both transactional (OLTP) and analytical (OLAP) use cases at scale; ensuring that application data is reliable and fast, streaming data is observable and fault-tolerant, and the analytical layer gives our data analysts and data consumers a clean, well-modeled foundation to build on.

Because we operate in a HIPAA-covered environment under SOC2 Type II certification, data security is embedded in every system you touch. You will be responsible for ensuring that the data platform reflects current security best practices; encryption, access control, audit logging, and least-privilege data access; and for partnering with compliance to support audits and risk assessments.

If you are energized by the challenge of building a coherent, scalable, secure data platform from the ground up in a fast-moving healthcare technology company, this role was designed for you.

DATA SECURITY IS A FIRST-CLASS ENGINEERING CONCERN HERE

We are a HIPAA-covered entity and SOC2 Type II certified organization. Every data system you design, every pipeline you build, and every access pattern you enable operates within our compliance boundary. Data security is not a checklist item — it is a core engineering requirement woven into the design of every system we ship.

The ideal candidate understands that in a regulated healthcare environment, building it right the first time is not just good engineering — it is a patient safety obligation. Experience with PHI handling, encryption standards, audit logging, and access control governance is a genuine differentiator in this role.

CORE TECHNOLOGY STACK

PostgreSQL - Apache Kafka - AWS SQS / SNS - dbt - AWS S3 - Python - Terraform - Docker / ECS - Data Catalog - GitHub Actions - HIPAA Controls - SOC2 Compliance

KEY RESPONSIBILITIES

Data Platform Architecture & Ownership

• Own the design, deployment, and ongoing evolution of the company's data platform; spanning transactional databases, streaming infrastructure, and analytical systems; as a unified, coherent architecture.
• Define and enforce data platform standards: naming conventions, schema versioning, migration practices, pipeline design patterns, and documentation requirements that the entire engineering organization can rely on.
• Partner with software engineering to define data contracts; agreed schemas, event formats, and API boundaries; that allow application teams and data teams to evolve independently without breaking downstream consumers.
• Evaluate and recommend tooling, infrastructure, and architectural patterns as the platform scales; lead proof-of-concept work for new data technologies with clear, evidence-based recommendations.

Operational Databases : PostgreSQL & OLTP

• Design, maintain, and optimize PostgreSQL schemas for transactional workloads — including index strategy, query tuning, connection pooling configuration, and table partitioning for high-volume tables.
• Own database migration management using version-controlled tooling (Flyway, Alembic, or equivalent); ensure all schema changes are reviewable, reversible, and deployed through CI/CD pipelines.
• Configure and maintain high-availability PostgreSQL infrastructure including replication, automated failover (RDS Aurora or equivalent), and point-in-time recovery; define and test RTO/RPO targets.
• Monitor database performance proactively using query analysis, slow query logs, and observability tooling; identify and resolve performance regressions before they impact application reliability.

Streaming & Event-Driven Systems : Kafka & SQS

• Design and manage Apache Kafka topic architecture including partition strategy, retention policies, consumer group design, and schema registry management (Avro / Protobuf / JSON Schema).
• Architect and maintain AWS SQS and SNS patterns for asynchronous service communication including dead-letter queue configuration, retry logic, message visibility, and poison message handling.
• Establish streaming system reliability standards lag monitoring, consumer health dashboards, and runbooks for common failure scenarios.

Analytical Systems : Data Warehouse & OLAP

• Design and maintain the company's data warehouse (Snowflake or Redshift) including storage architecture, compute warehouse strategy, cost optimization, and role-based access control.
• Build and maintain dbt models that transform raw operational data into clean, well-documented, business-ready data marts and dimensional models; enforce dbt coding standards, testing, and documentation coverage.
• Design and operate ELT pipelines that reliably move data from PostgreSQL, Kafka, and third-party SaaS systems into the analytical layer with data quality checks, freshness SLAs, and failure alerting.
• Build a self-service analytics foundation that enables data analysts and data scientists to work productively — including a well-governed data catalog, documented lineage, and query performance optimization.

Data Security : HIPAA & SOC2 Compliance

• Ensure all data systems transactional databases, streaming pipelines, and the analytical layer implement encryption at rest and in transit, least-privilege access control, and comprehensive audit logging that meets HIPAA and SOC2 requirements.
• Design and maintain role-based data access controls across the full platform including column-level and row-level security in the data warehouse for tables containing PHI or other sensitive fields.
• Own data retention and deletion workflows including PHI de-identification pipelines, retention schedule enforcement, and right-to-deletion support for applicable regulatory requirements.
• Support SOC2 audits and HIPAA risk assessments by maintaining up-to-date data flow documentation, access control evidence, and encryption configuration records.
• Partner with the IT Administrator and engineering leadership to respond to security incidents involving data systems including access anomaly investigation, breach containment, and post-incident remediation.

REQUIRED QUALIFICATIONS

• 4+ years of experience as a Data Engineer or Platform Engineer with demonstrated ownership across multiple layers of a production data platform — not a single pipeline or a single tool.
• Advanced PostgreSQL proficiency — hands-on experience with schema design for high-traffic transactional systems, index optimization, query tuning, and production database administration including replication and failover configuration.
• Hands-on experience with Apache Kafka in a production environment — topic design, consumer group management, offset handling, and schema registry. AWS SQS/SNS experience for asynchronous messaging patterns is also required.
• Data warehouse design and administration experience (Snowflake or Redshift preferred) — including storage design, compute cost management, RBAC, and dimensional modeling for analytical use cases.
• Proficiency with dbt — building production-grade transformation models, writing dbt tests, and managing model documentation and lineage in a team environment.
• Strong Python skills applied to data engineering: building pipelines with Pandas, PySpark, or SQLAlchemy; automating data quality checks; and scripting infrastructure or operational tasks.
• Experience building and operating data orchestration pipelines using Apache Airflow, AWS Step Functions, or an equivalent workflow orchestration tool.
• Working knowledge of HIPAA Security Rule requirements and SOC2 controls as they apply to data systems — including PHI handling, encryption standards, access control governance, and audit log requirements.
• Infrastructure-as-code familiarity (Terraform or AWS CDK) for provisioning and managing data infrastructure; comfort with CI/CD pipelines for data system deployments.
• Strong communication and documentation habits — you write clear data contracts, maintain data dictionaries, and produce architecture documentation that both engineers and non-technical stakeholders can navigate.

PREFERRED QUALIFICATIONS

• Strong Plus — Industry experience:

Prior data engineering experience in MedTech, Pharmaceuticals, Clinical Research, or Healthcare IT — particularly within a HIPAA-covered entity or SOC2-certified organization — is a significant differentiator. Familiarity with healthcare data standards (HL7, FHIR, ICD-10, CPT codes, or claims data formats) is highly valued.

• Experience with real-time analytics patterns — using Kafka Streams, Apache Flink, or AWS Kinesis for low-latency aggregation and stream processing beyond simple fan-out pipelines.
• Familiarity with data observability tooling — Monte Carlo, Great Expectations, Soda Core, or similar — for automated data quality monitoring and anomaly detection in production pipelines.
• Experience building or maintaining a data catalog and data lineage system — Atlan, Collibra, DataHub, or dbt's built-in lineage — enabling data discovery across a complex platform.
• AWS data services depth: hands-on experience with Glue, Lake Formation, Athena, MSK (Managed Kafka), or RDS Aurora beyond basic configuration.
• Exposure to ML/AI data infrastructure — building feature stores, managing training datasets, or designing data pipelines that feed model training and inference systems.
• Experience with column-level or row-level security in Snowflake or Redshift for PHI protection and multi-tenant data isolation.
• Contributions to data platform migrations — moving from a legacy database or warehouse to a modern stack, including data validation, cutover planning, and rollback strategy.