Success in this role means delivering reliable, scalable data solutions that enable timely decision-making, while proactively improving data quality, platform performance, and engineering practices.

The Manager, DataEngineeringisan individual contributor role. This role supports the creation, maintenance, and operational support of business-unit data assets, data mart solutions, reporting, and analytics that enable business leaders to make decisions and drive continuous process improvement. The role works across business and technical teams to define requirements, metric logic, and data definitions; develop and troubleshoot ETL/ELT logic using SQL and team tooling; complete testing; and provide ongoing maintenance and operational support for code, workflows, and utilities, including job monitoring, issue resolution, and user support.

We92re looking for ahands-on data engineerwith strongtechnical and business acumenwho performs well in a structured, fast-paced environment and is passionate about building high-quality, high-performance, and scalable solutions. This role is well suited for someone who is motivated to learn and understand corporate systems, reinforce engineering best practices, streamline existing processes, and contribute to a future-state data platform that improves client-centricity, speed to market, scale, and efficiency. The ideal candidate is comfortable creating structure, working through ambiguity, and adapting to change.

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.

• Designandbuilddimensional data models, including facts and dimensions, determine appropriate table grain, and implement slowly changing dimensions where historical tracking is required.

• Defineandimplementpractical 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

• Collaboratewith business stakeholders and technical teams totranslate business needsintoscalabletechnicalsolutions,includingmetric logic, and data definitions.

• Work closely with development partners, product owners, and team members to design features, decompose stories, and prioritizedelivery.

• Share technical knowledge and support team success through collaboration, documentation, and guidance.

Leadership & Influence

• Provide technical leadership for data pipeline development andengineering practices.

• Navigate cross-functional communication effectively to maintain alignment across teams.

• Use data-driven reasoningto 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 metadatamanagement practices to protect sensitive information.

• Contribute to a culture of open feedback, accountability, and continuous improvement.

Required Qualifications:

• Expertise in ETL/ELT development,SQL, anddataengineering bestquality practicesincluding data quality,testing, monitoring, andexception 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 Cloudtechnologies, includingBigQueryand Cloud Storage.

• Business analysis experience to translate business requirements into data mappings, metric logic, and data definitions, and toperform 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).

• Bachelor92s or master92sdegree 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 AIindevelopmentlifecycle, and enabling AI-ready data environments