Data Engineer

Data Engineering & Architecture

• Build and maintain data mart solutions that support reporting and analytics use cases.
• Design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data. Develop and troubleshoot ETL/ELT logic using SQL and team tooling.
• Design and build dimensional data models, including facts and dimensions, determine appropriate table grain, and implement slowly changing dimensions where historical tracking is required.
• Define and implement practical data retention and history strategies that preserve analytical value without overloading downstream reporting tools.

Data Quality, Reliability & Operations

• Implement and maintain data quality controls, reconciliation checks, testing, and monitoring to ensure data accuracy, consistency, and reliability.
• Support production reliability through job monitoring, issue resolution, root-cause analysis, operational support, and documentation.
• Create and maintain production support and deployment artifacts.

Collaboration & Delivery

• Collaborate with business stakeholders and technical teams to translate business needs into scalable technical solutions, including metric logic, and data definitions.
• Work closely with development partners, product owners, and team members to design features, decompose stories, and prioritize delivery.
• Share technical knowledge and support team success through collaboration, documentation, and guidance.

Leadership & Influence

• Provide technical leadership for data pipeline development and engineering practices.
• Navigate cross-functional communication effectively to maintain alignment across teams.
• Use data-driven reasoning to constructively challenge decisions, align on outcomes, and execute once direction is set.

Risk, Governance, & Continuous Improvement

• Identify technology risks and dependencies early and help establish mitigation plans.
• Implement data security, governance, and metadata management practices to protect sensitive information.
• Contribute to a culture of open feedback, accountability, and continuous improvement.

• Expertise in ETL/ELT development, SQL, and data engineering best quality practices including data quality, testing, monitoring, and exception handling.
• Strong understanding of data pipelines, data mart design, and common engineering patterns.
• Strong understanding of data warehouse concepts, including star schema, fact and dimension modeling, table grain, slowly changing dimensions, and operational data stores.
• Experience with Google Cloud technologies, including BigQuery and Cloud Storage.
• Business analysis experience to translate business requirements into data mappings, metric logic, and data definitions, and to perform data analysis.
• Minimum of 3 years of hands-on data engineering experience.
• Solid understanding of the data lifecycle, metadata management, and governance standards.
• Ability to recommend practical data retention and history strategies that balance analytical value with reporting performance.
• Strong cross-functional collaboration skills with leadership, colleagues, and stakeholders.
• Strong communication and stakeholder management skills across technical and non-technical audiences.
• Willingness to learn new skills and adapt to evolving technologies to meet future business needs.
• Proficiency with development tools including version control (for example, GitHub), project management software (for example, JIRA), and orchestration tools (for example, Control-M, SQL Server Integration Services, Informatica, or similar).
• Bachelor’s or master’s degree in computer science, information technology, or a related field, or equivalent practical experience.

Preferred Competencies

• 5+ years of experience with reporting and data visualization tools (Power BI, Tableau)
• 5+ years of experience with data management tools and coding languages (Python)
• 3+ years of experience in the financial services industry and/or a B2B environment
• Experience leveraging AI in development lifecycle, and enabling AI-ready data environments.