Description
Senior Data Engineer – Enterprise Data Enablement
The Enterprise Data Enablement team is seeking a Senior Data Engineer who can design, develop, and maintain secure, scalable, and efficient data pipelines and platforms. This role will focus on building and deploying data solutions across financial consumer business domains by leveraging existing and new data framework capabilities to acquire, transform, stream, and integrate data. The candidate will also contribute to innovative data engineering solutions, including AI/GenAI/Agentic AI-ready data capabilities, while collaborating with and supporting a team of data engineers in building scalable, secure, and intelligent data platforms.
Primary Responsibilities
- Design, build, and maintain reliable, efficient, and scalable data pipelines to acquire, transform, and store large datasets.
- Develop robust data pipelines to collect, process, and compute metrics from various financial data sources while adhering to quality and development standards.
- Contribute to application architecture and technical solutions, and help implement data framework patterns alongside senior engineers and architects.
- Collaborate with cross-functional teams to deliver optimal data solutions that meet business and platform needs.
- Develop and deploy high-quality, production-ready code.
- Apply strong database design principles and data modeling techniques to translate business requirements into scalable data solutions.
- Develop and optimize data models to support analytics, reporting, machine learning, and AI-driven use cases.
- Support the implementation and enhancement of enterprise data frameworks and contribute to scalable solutions.
- Identify opportunities to improve existing frameworks and help build reusable capabilities across the organization.
- Troubleshoot and resolve data-related issues in a timely manner.
- Execute unit testing for data pipelines, validate results, and ensure data quality and accuracy; partner with business users for User Acceptance Testing and support deployment activities.
- Follow change management practices and ensure adherence to compliance and regulatory standards.
- Design and build data pipelines and platform capabilities that support AI, Generative AI, and Agentic AI use cases, including model training, inference, retrieval, and orchestration workflows.
- Enable AI-ready data foundations by developing high-quality, governed, and reusable datasets for machine learning, large language model (LLM), and intelligent automation solutions.
- Develop and optimize pipelines for structured, semi-structured, and unstructured data to support GenAI use cases such as semantic search, document intelligence, and retrieval-augmented generation (RAG).
- Partner with data scientists, ML engineers, architects, and product teams to integrate AI/GenAI capabilities into enterprise data platforms and workflows.
- Implement metadata, lineage, governance, security, and access controls required for responsible AI and enterprise-scale GenAI adoption.
- Ensure observability, reliability, performance, and data quality for data pipelines, including those supporting AI-enabled workflows.
Education, Certifications, and/or Other Professional Credentials
- Bachelor’s degree in Computer Science, Engineering, or a related technology field
Hours and Work Schedule
- Hours per Week: 40
- Work Schedule: Monday through Friday
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