Contractor: Lead Analytics Engineer

Why You'll Love This Role: In this role, you will have the opportunity to directly shape our data ecosystem by building robust pipelines and applying modern software engineering principles like version control and automated testing. You’ll work closely with key stakeholders to drive data-driven decision-making, lead high-impact initiatives, and manage large-scale infrastructure and automation. If you thrive on transforming complex data into reliable, actionable insights while building scalable analytics systems, this position offers the ideal platform to showcase and expand your expertise. What You'll Be Doing: Data Modeling and Transformation Build new analyses and support existing ones using SQL and Python. Apply software engineering principles like version control and continuous integration to the analytics codebase. Expand our data warehouse with clean data ready for analysis. Data Quality and Testing Apply advanced data testing strategies to ensure resulting datastores are aligned with expected business logic. Implement validation checks and automated testing procedures to manage data quality in your ETL/ELT pipelines. Collaboration and Communication Work with stakeholders to define business logic and data expectations. Help drive a change in the usage of data by actively surfacing insights to stakeholders. Lead initiatives and problem definition, scoping, design, and planning. Infrastructure and Automation Build tools and automation to run data infrastructure. Manage large-scale data migrations in relational datastores. About You: 8+ Years experience working with data in a software environment. Required Skills: Mastered proficiency in SQL and Python; advanced experience managing business semantic layer tooling, data catalog tooling and data integrity testing frameworks. Experience with dbt orchestration and best practices. You have a track record of working autonomously and proactively, with deep domain knowledge of data systems. Required Tech Stack: SQL, Python, relational datastores, DAG tooling (like Dagster or Airflow), dbt and Tableau. Experience with cloud-based infrastructure (AWS, GCP, Terraform) and document, graph, or schema-less datastores.