Senior Data Engineer

RxSense LLC; Boston (MA); On-site; USD 150,000 - 190,000; Full time

Job summary

RxSense LLC is seeking a Senior Data Engineer in Boston to own the data platform end-to-end, building pipelines and governed models that power analytics and AI systems.

The role centers on Snowflake-based data platforms, DAG-based workflow orchestration with Airflow and dbt, and designing end-to-end data flows from ingestion through the gold layer while ensuring reliability, cost efficiency, and governance.

Qualifications

• 5+ years hands-on data engineering experience.
• Proven Snowflake data modeling and performance tuning.
• Hands-on Airflow or DAG-based orchestration experience.

Responsibilities

• Design, build, and maintain scalable data pipelines on Snowflake.
• Architect end-to-end data flows from ingestion to gold layer.
• Own platform reliability, performance tuning, cost efficiency.
• Establish standards: version control, CI/CD, testing, and documentation.
• Collaborate with analytics and BI on governance and data contracts.
• Champion data quality and governance across platform layers.

Skills

Data modeling

Pipeline design

Platform ownership

Tools

Snowflake

Apache Airflow

dbt

Job description

We are a healthcare technology company that provides platforms and solutions to improve the management and access of cost‑effective pharmacy benefits. Our technology helps enterprise and partnership clients simplify their businesses and helps consumers save on prescriptions.

As a leader in SaaS technology for healthcare, we offer innovative solutions with integrated intelligence on a single enterprise platform that connects the pharmacy ecosystem. With our expertise and modern, modular platform, our partners use real‑time data to transform their business performance and optimize their innovative models in the marketplace.

Position Summary

The Senior Data Engineer owns the data platform end to end — designing, building, and maintaining the infrastructure, pipelines, and governed data layers that power analytics and downstream consumers across the organization. This is a hands‑on engineering role centered on cloud data platform development, pipeline orchestration, and data modeling at scale.

The guiding principle is to build once, consume everywhere — define transformations and metrics a single time, governed and version‑controlled in the warehouse, then serve them consistently to every downstream consumer: BI tools, client‑facing reporting, and AI systems.

The ideal candidate is comfortable building data platforms and systems from scratch, thrive in a platform development and maintenance environment, and brings strong hands‑on experience with Snowflake and workflow orchestration tools such as Apache Airflow and dbt (or equivalent platforms). This role does not own BI visualization — your work spans raw ingestion through the governed gold layer.

Key Responsibilities

Data Platform Development & Maintenance

• Design, build, and maintain scalable data pipelines and platform infrastructure on Snowflake.
• Architect and implement end‑to‑end data flows from source ingestion through the silver and gold layers.
• Own platform reliability, performance tuning, and ongoing maintenance — treating the data platform as a product.
• Build systems from the ground up where needed; evaluate existing systems and refactor for scale, cost efficiency, and maintainability.
• Establish engineering standards: version control, CI/CD, testing frameworks, and documentation for all platform components.

Pipeline Orchestration & Workflow Management

• Design and maintain orchestration workflows using Apache Airflow (or equivalent) to schedule, monitor, and manage data pipelines.
• Ensure pipeline reliability, observability, and failure recovery through alerting, logging, and automated retries.
• Coordinate dependencies across upstream sources and downstream consumers with well‑defined SLAs.

Gold‑Layer Modeling & Metrics

• Design, build, and maintain gold‑layer data models and a governed metrics layer in Snowflake using dbt — clean, tested, documented, and version‑controlled.
• Establish metric definitions as the single source of truth: calculated once, reused everywhere, with clear ownership and change management.
• Push transformation and calculation logic upstream into the warehouse so the consumption layer stays simple, consistent, and high‑trust.
• Partner with analytics and BI stakeholders on the boundary between pipelines (data engineering) and consumption (analytics/reporting).
• Structure and document data models so they meet the governance and consistency requirements of AI agents and autonomous analytical systems.

AI‑Native Development

• Build with coding agents and AI‑assisted tooling as a primary part of the workflow — accelerating pipeline development, data modeling, documentation, and testing.
• Independently set up and manage development environments, repositories, and the tooling needed to work effectively with agentic systems.
• Help establish team practices and standards for building data infrastructure in the AI era.

Quality, Governance & Platform Judgment

• Champion data quality, consistency, and trust across all platform layers — ensuring data holds up under scrutiny from any downstream consumer.
• Contribute to platform architecture decisions; identify opportunities for automation, templating, and process improvement.
• Apply sound engineering judgment to evaluate build vs. buy decisions and tooling trade‑offs.

Qualifications

Required

• 5+ years of hands‑on experience in data engineering — building pipelines, data models, and platform infrastructure in a data‑driven organization.
• Snowflake:Proven, hands‑on production experience building data models and transformations in Snowflake. Deep familiarity with Snowflake architecture, performance optimization, and cost management.
• Workflow Orchestration (must have):Hands‑on experience with Apache Airflow or an equivalent DAG‑based orchestration platform (e.g., Prefect, Dagster