- Works with business stakeholders and IT to translate business logic into scalable data and analytic solutions
- The Financial Services Data Engineers work as a centralized, but dedicated team. The lead data engineer will primarily support a specific Financial Services team directly but will also support other teams as needed
- Works with business stakeholders to ensure Financial data is decision ready and reliably meets quality standards defined by stakeholders throughout the data lifecycle
- Develops ETL pipelines, typically leveraging existing patterns as available for specific project execution
- Identifies scalable approaches for resolving data quality or consistency issues
- Works with various Financial Services stakeholders to understand, document, and scope process improvements needed for new and existing financial data pipelines
- Maintains a solid understanding of the Client suite of data enginnering tools (dbt, AWS, Apache Airflow, Databricks, Alteryx, API’s)
- Leads implementation of new capabilities, processes, and technical patterns within Financial Services
- Leads and is responsible for multiple data projects across Financial Services
- Works with IT and Enterprise Analytics to develop data models to enable data science and analytical requests from Financial Services staff
- Owns and is accountable for data model and code quality as well as relevant documentation
- Learns and maintains business context of the Financial Services team they are supporting through data
Minimum Qualifications
- Strong analytical and data modeling skills
- Solid understanding of database technology
- Strong skills in SQL and Python
- Experience using GitHub or similar code repository
- Fast learner and proven problem solver
- Excellent oral and written communication skills
- Business-results and customer-focused orientation. Seeks to understand business needs and works to anticipate, identify, and meet end-user needs
Preferred Qualifications ( but are a great importance to the client for the Data Architecture. )
- Experience using the AWS big data technology stack, e.g., AWS S3, Redshift
- Experience in Apache Airflow
- Experience using data visualization tools
- Experience implementing data governance principles
- Experience with Oracle (ERP) Financial modules
- Experience with dbt
- Experience with Databricks