Senior Data Engineer (US - Remote)

Who Are We, and Why May You Want to Join Us?

Astrodata is a boutique data consultancy founded by long-time practitioners with deep experience in the modern data stack. We specialize in designing and implementing robust analytics and AI architectures - often supporting customer-facing data applications - with a strong track record in healthcare and other data-sensitive industries.

We work across a diverse portfolio of clients in healthcare, fintech, e-commerce, education, and AI innovation. This provides our team with exposure to a wide range of industries and technical challenges. As a member of our team, you'll collaborate with experienced consultants and cutting-edge technologies, gaining meaningful opportunities to grow as both a technical expert and strategic communicator. You’ll fine-tune your knowledge of the complex trade-offs between agility and governance in launching modern data and AI solutions.

We value the unique perspectives our team and clients bring to every engagement. At Astrodata, we strive to build shared understanding, mutual respect, and sustainable growth - all while delivering impactful data solutions for our clients.

What Are We Looking For?

We’re looking for a driven, thoughtful, and intellectually curious Senior Data Engineer to join our core team at Astrodata.

In this role, you’ll focus on designing and implementing data solutions that support ingestion, transformation, orchestration, and downstream delivery - serving a wide range of clients and technology partners.

You’ll work closely with analytics engineers, software engineers, data analysts, and ML/AI engineers to solve complex data challenges and make informed architectural decisions. Depending on the client and project, you might:

  • Build and orchestrate purpose-driven data pipelines and dimensional model outputs to enable self-service analytics, AI and business intelligence.

  • Support the productionization of downstream data delivery, including but not limited to: embedded data applications, reverse ETL, ML features and AI products.

  • Help define and release scalable architectures that balance agility with governance.

  • Provide strategic guidance and thought leadership on scaling data and AI solutions.

At Astrodata, we value empathy not just for our clients, but for each other. Our ability to collaborate effectively is essential to both our business and team culture. We’re looking for someone who combines technical excellence and intellectual curiosity with strong communication and collaboration skills. The ideal candidate listens first, can translate business goals into technical requirements (and vice versa), and brings enthusiasm for learning and applying modern data technologies in a fast-evolving ecosystem.

If you’re energized by a mix of technical ownership, collaborative problem-solving, and thoughtful consulting work - you’ll feel at home here.

What You’ll Do at Astrodata

Data Architecture & Pipeline Engineering

  • Design, implement, and maintain scalable and modular data pipelines using tools like dbt and Airflow, ensuring reliable ingestion, transformation, and delivery across diverse client environments.

  • Architect and optimize batch and streaming workflows using platforms such as Snowflake, MotherDuck, Kafka, and AWS-native services (e.g., S3, Lambda, ECS Fargate), balancing performance, cost, and maintainability.

  • Lead the development and orchestration of robust ETL/ELT frameworks, with a focus on reusability, observability, and testing practices (e.g., data contracts, dbt tests, test coverage, enabling teams to employ robust data quality and testing practices).

  • Proactively implement monitoring, logging, CI/CD practices, and alerting strategies to ensure pipeline health and resilience in production.

Client Enablement & Cross-functional Partnership

  • Serve as a strategic technical partner to client stakeholders across operations, finance, product, and executive teams - translating ambiguous needs into actionable data solutions.

  • Guide client teams on data best practices, system design tradeoffs, and how to leverage the modern data stack effectively.

  • Collaborate with client-side analysts, data scientists, and engineers to design shared data models, semantic layers, and analytics-ready datasets.

Data Quality, Governance & Security

  • Define and enforce data validation and automated quality checks across pipelines and layers of the stack.

  • Support client data classification, access controls, and security best practices for protecting sensitive data in both transit and at rest.

  • Establish and maintain metadata, documentation, and lineage tracking systems to ensure transparency and stewardship across evolving data ecosystems.

Leadership, Mentorship & Continuous Improvement

  • Provide technical leadership within project teams, mentoring junior engineers and championing high standards in code quality, testing, and review processes.

  • Stay current on evolving data tools and patterns; proactively propose new architectures and workflows to meet emerging client needs.

  • Lead or contribute to internal tooling, templates, and process improvements that strengthen the consultancy’s data engineering practice.

Requirements:

  • 5+ years of experience as a Data Engineer (or similar role)

  • Significant experience designing and implementing data warehouse solutions end-to-end (e.g. Snowflake architecture and platform delivery) - from data ingestion and data lake design patterns, to release of data marts

  • Strong SQL experience, especially writing data transformations via dbt

  • Strong Python experience, especially authoring data pipelines and API integrations

  • Experience dimensional modeling (e.g. especially Kimball, others like Data Vault helpful)

  • Experience scaling batch and streaming analytics architectures

  • Experience working with Python-based workflow orchestration for tools (e.g. Airflow, Prefect) for data ingestion and/or other data orchestration processes.

  • Experience using git for version control, and working within agile (e.g. Scrum) teams.

Nice to Haves:

  • Experience developing a semantic layer, and releasing semantic models to production

  • Experience with containerization: Docker development and orchestration (e.g. ECS Fargate, Kubernetes) patterns

  • Experience productionizing data architecture serving downstream embedded analytics or AI applications

  • Experience with additional data warehouse & platform technologies (e.g. Databricks, ClickHouse, DuckDB)

  • Experience with streaming event analytics pipelines and platforms (e.g. Snowplow)

  • Experience working with ML teams productionizing ML / AI workloads

  • Experience working in healthcare or health technologies

  • Experience working on marketing attribution data challenges, especially data-driven attribution and marketing mix modeling (MMM).

Can I work remotely?

Yes. This role is for a US-based candidate. Candidates from any US timezones are welcome to apply!

Benefits

  • Remote work with at least annual team offsite

  • Great health, dental, and vision coverage

  • 401k benefits with employer matching contribution

  • The chance to help build out our practice, helping form our service offerings and strategy

  • Budget for purchasing home workspace hardware and software

  • Ability to learn rapidly amongst an extremely talented and collaborative team

  • An awesome, empathetic community of co-workers

How To Apply

Email your resume and cover letter to careers@astrodata.us

At Astrodata, we succeed and bolster an empathetic, collaborative culture built off our diversity of backgrounds, experiences, and breadth of technical expertise and perspectives. We will never discriminate against any job candidate or employee due to age, race, ethnicity, religion, sex, color, national origin, gender, gender identity, sexual orientation, medical condition, marital status, parental status, disability, or Veteran status.