Finance Analytics Engineer

As an Analytics Data Engineer within the Finance team, you will be a key player in shaping our data ecosystem. You will design, build, and maintain scalable data pipelines that transform raw data into a reliable and trustworthy source of truth for the Finance organization and beyond. 

You will be the primary liaison between the Finance and the Data Engineering teams. You will use your domain knowledge of finance to understand their data requirements, translate them into technical specifications, and then build the necessary data infrastructure. Your work will directly impact financial planning, forecasting, and strategic decision-making across the company.

You Are:

• A Data Expert: You have a deep understanding of data warehousing, ETL/ELT principles, and data modeling best practices. You can design and implement robust and scalable data pipelines.
• A Problem-Solver: You are passionate about using data to solve complex business problems. You can break down ambiguous requirements into clear, actionable data engineering tasks.
• Collaborative: You enjoy working with diverse teams and can effectively communicate complex technical concepts to both technical and non-technical stakeholders.
• Detail-Oriented: You have a high bar for data quality and integrity. You take pride in building accurate and reliable data products and you are comfortable owning full quality from end to end.
• Curious and AI-forward:  You are excited by the potential of LLMs, prompt engineering, and the rapidly changing world of AI tooling. You go beyond just using tools like Claude/Claude Code or Cursor—you've experimented with them and are keen to build and integrate entire systems around them.

You Will:

• Design, develop, and maintain robust data pipelines using Python and Apache Airflow to process large volumes of data from various sources (e.g., internal databases, third-party APIs, and external systems).
• Be the subject matter expert on our Redshift data warehouse, optimizing queries and tables, and ensuring data integrity.
• Build and maintain data models that provide a single source of truth for key business metrics and KPIs. 
• Co-develop and implement data quality checks and monitoring with finance business process owners to ensure the accuracy and reliability of our data.
• Document data pipelines, data models, and business logic to ensure maintainability and transparency. 
• Collaborate with Finance stakeholders to understand their data needs and translate them into scalable data solutions.
• Partner with Finance and Data teams to create the AI Business Context layer for financial applications, converting accounting principles, metric definitions, and business logic into structured, AI-interpretable formats.
• Build tools that provide natural language access to financial data, automated anomaly detection and AI-assisted reporting, enabling Finance stakeholders to independently discover insights.

You Have:

• A Bachelor's degree in Computer Science, Engineering, or a related quantitative field.
• 4+ years of experience in a data-focused role, with a strong emphasis on data engineering, business intelligence, or a similar analytics function.
• Proven experience in designing, building, and maintaining production-level data pipelines.
• Demonstrable experience working directly with finance or business operations teams to deliver data solutions.
• Expertise in SQL and experience with a modern data warehouse, preferably Redshift.
• Strong programming skills in Python.
• An interest in learning core financial modeling concepts to understand data use-cases