Senior Analytics Engineer

USA, Canada, Mexico, Argentina - Remote

What Are We Looking For?

We’re looking for a driven, thoughtful, and intellectually curious Senior Analytics 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 data 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.

  • Excellent English language skills

  • Consulting experience preferred

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 Modeling & DAG Engineering

  • Design, implement, and maintain scalable and modular data transformation pipelines using tools like dbt and Airflow, ensuring reliable transformation, data quality enforcement, modeling layer construction, and delivery of robust consumption-ready data across diverse client environments.

  • Architect and optimize a flexible, business and agentic-friendly semantic layer for consistent downstream consumption - with consideration for performance, stakeholder needs and reliable analytical outputs.

  • 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 analytics engineering and modeling best practices, agile yet trustworthy delivery patterns, 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

  • Design, define and enforce data validation and automated quality expectations and checks across data modeling layers and components of the stack, including data contracts and effective delivery downstream.

  • Implement data governance standards and practices within data model releases and team procedures, setting up clients for long-term success.

  • 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 analytics engineering practice.

Requirements:

  • 5+ years of experience as an Analytics Engineer (or similar role)

  • Significant experience designing and implementing dimensional modeling solutions end-to-end (e.g. planning and building staging models, conformed layers and marts) - from the design and agile development, to robust data testing and release management

  • Strong SQL experience, especially writing data transformations via dbt

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

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

  • Experience working in BI tools, especially those which include or integrate directly with a semantic layer (e.g. Omni, Looker)

  • Python experience, especially orchestrating data pipelines via a workflow orchestration tool (e.g. Airflow, Prefect)

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

Nice to Haves:

  • Experience productionizing data models used for serving downstream embedded analytics or AI applications

  • Understanding of the medallion architecture, building incremental history capture pipelines and change data capture (CDC) patterns in dbt

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

  • Experience with streaming event analytics pipelines and analytical use case (e.g. Snowplow data, clickstream data,  analytics)

  • Experience working in healthcare or health technologies (and the security and privacy considerations for building solutions in such environments)

  • 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 (US only)

  • 401k benefits with employer matching contribution (US only)

  • 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.