Senior Data Engineer

RxSense

2d•$145,000 - $165,000

About The Position

The Senior Data Engineer owns the data platform end to end, designing, building, and maintaining infrastructure, pipelines, and governed data layers that support analytics and downstream consumers across the organization. This hands-on engineering role focuses on cloud data platform development, pipeline orchestration, and data modeling at scale. The role follows a “build once, consume everywhere” approach: transformations and metrics are defined once in a governed, version-controlled warehouse and served consistently to BI tools, client-facing reporting, and AI systems. The position centers on Snowflake platform development and maintenance, workflow orchestration with Apache Airflow or equivalent tools, and gold-layer data modeling using dbt or equivalent platforms. It spans raw data ingestion through the governed gold layer and does not own BI visualization. The engineer will build and maintain platform systems, improve reliability and performance, establish engineering standards, and help shape practices for developing data infrastructure with AI-assisted tools.

Requirements

At least 5 years of hands-on data engineering experience building pipelines, data models, and platform infrastructure in a data-driven organization.
Proven, hands-on production experience building data models and transformations in Snowflake, including deep familiarity with its architecture, performance optimization, and cost management.
Hands-on experience with Apache Airflow or an equivalent DAG-based orchestration platform such as Prefect, Dagster, or Mage, including designing, deploying, and operating complex workflows.
Production experience using dbt for transformation modeling, testing, and documentation in a warehouse environment; equivalent semantic-layer tooling experience is considered.
Strong SQL proficiency, including writing, optimizing, and reviewing complex queries across large datasets.
Experience building data platforms or systems from scratch, including designing and standing up new infrastructure.
Comfort with long-term platform development and maintenance ownership, beyond project delivery.
Fluency with AI-era development practices, including building with coding agents, setting up development environments, and using AI tooling as a core productivity multiplier.
Familiarity with software engineering practices, including Git version control, CI/CD pipelines, automated testing, and code review.
Strong communication skills and ability to translate business requirements into technical data platform decisions.

Nice To Haves

Experience in pharmacy benefits management, healthcare technology, or a related regulated industry.
Experience with cloud infrastructure and data platform tooling on AWS, Azure, or GCP alongside the data stack.
Familiarity with data governance frameworks, data contracts, or data mesh principles.
Experience with streaming or near-real-time data pipelines, such as Kafka, Kinesis, or Spark Streaming.
Exposure to semantic or metrics-layer tooling such as MetricFlow, Cube, or LookML.
Experience building demo environments or client-facing data products.

Responsibilities

Design, build, and maintain scalable data pipelines and platform infrastructure on Snowflake.
Architect and implement end-to-end data flows from source ingestion through silver and gold layers.
Own platform reliability, performance tuning, and ongoing maintenance.
Build new systems where needed and evaluate or refactor existing systems for scale, cost efficiency, and maintainability.
Establish version control, CI/CD, testing, and documentation standards for platform components.
Design and maintain Apache Airflow or equivalent orchestration workflows 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.
Design, build, and maintain clean, tested, documented, version-controlled gold-layer data models and a governed metrics layer in Snowflake using dbt.
Establish metric definitions as a single source of truth, with clear ownership and change management.
Move transformation and calculation logic upstream into the warehouse so consumption layers remain simple and consistent.
Partner with analytics and BI stakeholders on the boundary between data engineering pipelines and analytics or reporting consumption.
Structure and document data models to meet governance and consistency requirements for AI agents and autonomous analytical systems.
Use coding agents and AI-assisted tools to accelerate pipeline development, data modeling, documentation, and testing.
Set up and manage development environments, repositories, and tooling for working effectively with agentic systems.
Help establish team practices and standards for building data infrastructure in the AI era.
Champion data quality, consistency, and trust across platform layers.
Contribute to platform architecture decisions and identify opportunities for automation, templating, and process improvement.
Evaluate build-versus-buy decisions and tooling trade-offs using sound engineering judgment.