<p>JobID: 210686288</p> <p>Category: Analytics Solutions & Delivery</p> <p>JobSchedule: Full time</p> <p>Posted Date: 2025-12-10T17:11:47+00:00</p> <p>JobShift:</p> <p>Base Pay/Salary: New York,NY $104,500.00-$165,000.00</p> <p>We are looking for a leader who is passionate about using data to accelerate product development, drive business growth, and improve the Chase customer experience.</p> <p>As a Data Owner Senior Associate, within the Card Data and Analytics Data Owner team, you will be a key part of the team helping us to deliver critical business initiatives and have data engineering skills where you will meticulously document and analyze data flows across various systems, ensuring seamless integration and consistency. You will also collaborate with stakeholders, provide valuable insights into data-related challenges and opportunities and continuous improvement in data processes.</p> <p>Job Responsibilities:</p> <ul> <li>Work closely with business analysts, data engineering, product teams, and other stakeholders to gather requirements, resolve data issues, and provide insights into data-related challenges and opportunities. </li><li>Partner with key business leaders to promote understanding of data usage, provide subject matter expertise, and ensure data is well-documented and classified with appropriate metadata and execute data-related tasks, manage milestones, and address risks, bottlenecks, and inefficiencies in the data development lifecycle. </li><li>Investigate data attributes and metrics, map their relationships and dependencies across systems, and assess their impact on business processes, reporting, and analytics and accurately document data flows, lineage, transformation, and movement across systems, ensuring clarity and comprehensiveness. </li><li>Create and update relational data models to support current and future data architecture, business objectives, and data governance. </li><li>Develop key performance indicators (KPIs) to measure progress toward data flow goals, and track work streams to ensure successful delivery and create plans for developing and delivering curated datasets to support strategic objectives, operations, analytics, AI/ML training, and reporting. </li><li>Assist Data and Analytics leads by identifying and integrating required data into analytics platforms for project support. </li><li>Identify, monitor, and mitigate data risks, including data protection, retention, destruction, storage, use, and quality and document and coordinate requirements for data accuracy, completeness, and timeliness; manage execution of tasks to comply with firm wide data integrity and protection policies. </li></ul> <p>Required Qualifications, Capabilities, and Skills:</p> <ul> <li>Bachelor's degree required, with 7+ years of industry experience in a data-related field. </li><li>Data Querying capabilities such as SQL, Python, or R, to develop and maintain data models and mappings. </li><li>Proficiency in data engineering techniques, including data extraction, transformation, and loading (ETL) processes, to support robust data management and integration. </li><li>Technical expertise in data management, data engineering, data pipelines (e.g. Data Flow Diagrams), data modeling (e.g. Entity Relationship Diagrams), data architecture, and governance, along with proficiency in data modelling tools (e.g. Erwin) and associated techniques. </li><li>Experience with cloud platforms like AWS, Google, or Azure, and/or big data technologies such as Hadoop, Spark, Alteryx, Snowflake. </li><li>Strong understanding of data quality, privacy, and security standards, and experience with data governance frameworks and tools. </li><li>Proven ability to manage delivery across multiple work streams with varying timelines, ensuring our product and organization meet goals through effective execution via internal partnerships. </li></ul> <p>Preferred Qualifications, Capabilities, and Skills:</p> <ul> <li>Subject matter expertise in business or product data or processes; Credit Card domain knowledge is a plus. </li><li>Knowledge of industry best practices for data management and modelling. </li></ul>