Analytics Engineer, LookML

Our client, a financial services company that specializes in credit reporting, is hiring an Analytics Engineer specialized in Looker to join their business intelligence team remotely! This team is modernizing its business intelligence and data modeling environment, and as an engineer on this team, you would refactor and rebuild semantic layers and models in Looker and support their migration efforts. Requirements: • Bachelor’s degree in a quantitative or technical field (e.g., Computer Science, Engineering, Mathematics, Statistics, or related discipline). • 3+ years of experience in analytics engineering, data engineering, or a related role. • Advanced proficiency in SQL and a strong understanding of dimensional modeling and data warehousing concepts. • Hands-on experience with BI tools, particularly Looker (LookML development, explores, and dashboarding). • Experience refactoring or rebuilding BI layers and semantic models in complex environments. • Experience supporting or executing BI migrations (e.g., dashboard rebuilds, metric standardization, model redesign). • Familiarity with modern data stack tools (e.g., dbt, Airflow, or similar). • Experience working with cloud data warehouses such as Snowflake and/or Redshift. • Strong ability to work with ambiguous or complex legacy systems and drive simplification. • Excellent communication skills and ability to collaborate with both technical and non-technical stakeholders. • Experience with data replication or integration (e.g., MySQL to cloud data warehouse) is a plus. • Familiarity with Python or other scripting languages is a plus. Responsibilities: • Design and develop scalable, well-structured data models to support business intelligence and analytics use cases. • Refactor and simplify existing BI semantic layers (e.g., Looker explores / LookML) to improve usability, performance, and maintainability. • Lead and support BI migration efforts, including rebuilding dashboards and underlying data models in alignment with updated data architecture. • Translate business requirements into clean, reliable datasets that enable self-service analytics. • Partner with cross-functional stakeholders (Product, Marketing, Finance, BI, etc.) to define metrics, standardize logic, and ensure consistent reporting. • Build and maintain ELT pipelines to transform and integrate data from multiple sources. • Support data platform migration and replication efforts, ensuring data accuracy, completeness, and integrity across systems. • Identify and resolve data quality issues, inconsistencies, and performance bottlenecks. • Establish and promote best practices for data modeling, documentation, and governance.