Data Engineer, Finance Data & BI

About the role

The Data Engineer will design, build, and optimize scalable data pipelines and architectures that transform raw financial data into high-quality, analytics-ready, and AI-enabled datasets. These datasets support strategic reporting, planning, forecasting, automation, and advanced analytics across the organization.

Responsibilities

• Solve complex problems across the full data stack, from advanced data wrangling (SQL, Python, Spark, or similar) to delivering stakeholder-ready, production-scale data solutions
• Design new architectures and reengineer existing ones, including optimized data structures, relational databases, and database code
• Develop, construct, test, and maintain robust, scalable, and efficient ETL/ELT pipelines using modern cloud technologies that support advanced analytics and AI/ML workloads
• Develop and maintain database code, including stored procedures, functions, and performance-optimized transformations
• Create and maintain ETL processes and contribute to CI/CD deployment workflows using GitHub Actions or similar tools
• Implement and optimize data models (e.g., dimensional modeling) within the Finance data environment
• Optimize data architecture for performance, scalability, and cost efficiency across large financial datasets
• Design and implement automated data quality checks, anomaly detection, and validation processes to ensure accuracy and trust in downstream analytics and AI use cases
• Ensure all data solutions meet financial governance and compliance standards, including SOX requirements
• Partner closely with Finance teams (Accounting, FP&A), BI, and Data Product partners to translate complex business requirements into scalable technical solutions
• Communicate technical concepts clearly to non-technical stakeholders, balancing innovation with operational risk and controls
• Enable trusted data environments required for forecasting models, scenario planning, and AI-driven insights
• Contribute to the strategic evolution of the Finance data platform by evaluating and piloting emerging tools and technologies

Requirements

• 4+ years of relevant experience
• 4+ years of technical and professional experience as a Data Engineer
• 4+ years of hands-on experience with data warehouse solutions, cloud platforms, relational databases, and data visualization or dashboarding tools
• Strong proficiency in object-oriented programming languages such as Python, Java, or C#
• Demonstrated experience with Google Cloud Platform (GCP) preferred over other platforms (e.g., Snowflake, Databricks, Microsoft, Teradata)
• Proven experience in an enterprise environment with:
• Building and optimizing cloud-based data solutions
• Supporting business-critical systems
• Designing or supporting production-scale AI/ML data pipelines
• Applying data governance by design
• Data warehousing and ETL best practices
• CI/CD and version control using GitHub

Qualifications

• Experience with PySpark
• Experience with Matillion or other modern ETL tools
• Working knowledge of core financial data concepts (general ledger, chart of accounts, financial reporting)
• Experience deploying SOX-compliant data solutions in a regulated enterprise
• Experience with Oracle JD Edwards or familiarity with financial application data integrations and data flows

Skills

• Nice to have: Experience with PySpark
• Nice to have: Experience with Matillion or other modern ETL tools
• Nice to have: Working knowledge of core financial data concepts (general ledger, chart of accounts, financial reporting)
• Nice to have: Experience deploying SOX-compliant data solutions in a regulated enterprise
• Nice to have: Experience with Oracle JD Edwards or familiarity with financial application data integrations and data flows