ABOUT THE JOB

As a Senior Analytics Engineer, you are the architect and owner of our central data foundation, working alongside a diverse team of analytics engineers, data engineers, data analysts, and data scientists. Right now, data is scattered across multiple systems. Your mission? Eliminate “multiple truths” once and for all and establish a single source of truth across the company.

You won’t just build reliable data models for business analysts and finance teams; you’ll also design the architecture powering advanced AI applications, like our agentic LLM tool. Serving as the primary technical bridge between analysts, data scientists, and platform engineers, you won’t wait for explicit daily instructions. You take high-level business goals, ask the right probing questions, and independently translate them into robust, production-ready solutions. Got a good idea or a smarter solution? At BridgeFund, you can take it from idea to production in no time. That speed of execution is one of the best parts of working in our data team.

Your responsibilities

• Design, build, and maintain complex, production-grade data models in Databricks (Gold layer) focused on performance, reliability, and reusability.

• Lead requirements sessions with business stakeholders (Finance, Product), translate ambiguous needs, and act as the primary liaison to Platform and AI engineering teams.

• Solve live and historical data integration challenges to eliminate conflicting metrics across systems.

• Model data from CDC sources to capture full audit histories and change records.

• Build automated quality checks, monitoring dashboards, and alerting systems, implement consistent naming conventions, and tag models in Unity Catalog.

• Play a key role in defining uniform, company-wide metrics (a layer on top of our data registries) and actively align definitions and interpretations across the business, spotting and resolving inconsistencies like conflicting definitions.

• Champion software engineering best practices, including version control, automated testing, and CI/CD pipelines.

What you bring

• At least 5 years of hands-on experience in Data Engineering or Analytics Engineering.

• Production-level experience with SQL, Python, and CI/CD pipelines. Working experience with Databricks (or PySpark) is a plus.

• Experience with frameworks like dbt, Kimball or Databricks to structure data models.

• You thrive on self-leadership. You don’t need detailed hand-holding to define required steps and execute.

• You understand how technical definitions affect decisions and communicate fluently with non-technical stakeholders.

• Experience in fast-paced scale-up environments where roles have broad scope and require working across domain boundaries.

Are you interested in this position?

Apply by clicking on the “Apply Now” Button below!

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