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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** RxSense LLC - **Location:** Boston, MA, United States - **Experience:** Expert - **Salary:** $145,000.0 - $165,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Big Data, Mobile Application Development, Cloud Computing, Code Review, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Document-Oriented Databases, Performance Tuning, Data Mesh, Software Engineering, Data Streaming, Technical Data Management Systems, Workflow Management Systems, Data Logging, Snowflake, AI Coding Agents, Agentic-AI, Git, Data Layers, Templating, Data Analytics, Apache Kafka, Build Tools, Spark Streaming, Data Management, Software Version Control, Data Pipelines - **Published:** October 20, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=941367b09c88db1e ## About the Role * 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, Mage). Ability to design, deploy, and operate complex pipeline workflows. * dbt: Demonstrated experience using dbt for transformation modeling, testing, and documentation in a production warehouse environment. Equivalent semantic layer tooling experience considered. * Strong SQL proficiency - able to write, optimize, and review complex queries across large datasets. * Experience building data platforms or systems from scratch - not just extending existing infrastructure but designing and standing up new ones. * Comfort with platform development and maintenance as a long-term ownership model - not just project delivery. * Fluency working in the AI era: building with coding agents, setting up development environments, and using AI tooling as a core productivity multiplier. * Familiarity with software engineering practices: version control (Git), CI/CD pipelines, automated testing, and code review. * Strong communication skills - able to translate business requirements into technical data platform decisions. Preferred * Experience in pharmacy benefits management, healthcare technology, or a related regulated industry. * Experience with cloud infrastructure and data platform tooling (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 (e.g., Kafka, Kinesis, Spark Streaming). * Exposure to semantic/metrics layer tooling (e.g., MetricFlow, Cube, LookML). * Experience building demo environments or client-facing data products. ## Description 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., 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.