Data Engineer

Responsibilities Include 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. What you have Required Qualifications 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. Pay: $30.00 - $45.00 per hour Work Location: In person