Sr Developer - Data Eng at Ally in Detroit, MI

Job description

Ally and Your Career Ally Financial only succeeds when its people do - and that’s more than some cliché people put on job postings. We love this stuff! We see our people as, well, people - with interests, families, friends, dreams, and causes that are all important to them. Our focus is on the health and safety of our teammates as well as work-life balance and diversity and inclusion. From generous benefits to a variety of employee resource groups, we strive to build paths that encourage employees to stretch themselves professionally. We want to help you grow, develop, and learn new things. You’re constantly evolving, so shouldn’t your opportunities be, too? Work Schedule : Ally designates roles as (1) fully on-site, (2) hybrid, or (3) fully remote. Hybrid roles are generally expected to be in the office a certain number of days per week as indicated by your manager. Your hiring manager will discuss this role's specific work requirements with you during the hiring process. All work requirements are subject to change at any time based on leader discretion and/or business need. 
 The Opportunity We are looking for a Senior Data Engineer to design, build, and optimize scalable data pipelines and data platforms supporting analytics, reporting, and AI/ML use cases. The ideal candidate has strong hands-on experience with Snowflake on AWS, Python-based ETL/ELT development, and enterprise scheduling/orchestration tools like Control-M, along with legacy/enterprise ETL experience in IBM DataStage (Good to Have). You will collaborate across engineering, analytics, and business teams in an Agile delivery model.

 The Work Itself Key Responsibilities Design, develop, and maintain end-to-end data pipelines (batch and near real-time) using Snowflake, AWS services, and Python. Build and optimize data models in Snowflake (e.g., dimensional modeling, data vault, or curated data marts) for analytics and downstream consumption. Develop and maintain ETL/ELT workflows using Python and IBM DataStage; migrate/modernize workloads where applicable. Implement job scheduling, monitoring, and operational support using Control-M (alerting, retries, SLAs, and dependency management). Ensure data quality, governance, lineage, and documentation standards are met across pipelines. Perform performance tuning and cost optimization across Snowflake and AWS (query optimization, clustering, warehouse sizing, storage management). Partner with stakeholders (Data Science/AI, BI, Product, and Platform teams) to enable data products and AI-ready datasets. Participate in Agile ceremonies, contribute to estimation, planning, and sprint execution; follow SDLC and change management processes. Troubleshoot production issues, perform root-cause analysis, and drive preventative improvements. Required Technical Skills Snowflake: Strong expertise in Snowflake architecture, SQL development, performance tuning, security/roles, data loading/unloading, and best practices. AWS: Hands-on experience with AWS data ecosystem (commonly S3, IAM, CloudWatch; plus services such as Glue, Lambda, EC2, Step Functions, EMR, or Kinesis as applicable). Python: Strong Python programming for data engineering (ETL/ELT frameworks, API ingestion, automation, unit testing, logging). Control-M: Experience designing and managing enterprise job scheduling, dependencies, calendars, SLAs, monitoring, and incident handling. SQL: Advanced SQL skills for transformations, optimization, and data validation across large datasets. CI/CD & Version Control: Experience with Git and CI/CD practices for data pipelines (tools may vary). Operational Excellence: Monitoring, alerting, and production support experience in a 24x7 or business-critical environment. Good to Have AI/ML exposure: Experience enabling AI/ML pipelines or feature datasets; familiarity with ML lifecycle concepts, feature engineering, or MLOps tools/processes. IBM DataStage: Solid experience building and maintaining DataStage jobs, handling c