Sr Developer - Data Eng

Ally•Detroit, MI

3d•$85,000 - $150,000

About The Position

Ally Financial is seeking a Senior Data Engineer to design, build, and optimize scalable data pipelines and platforms supporting analytics, reporting, and AI/ML use cases. The role calls for hands-on expertise with Snowflake on AWS, Python-based ETL/ELT development, and enterprise scheduling and orchestration tools such as Control-M, with IBM DataStage experience considered beneficial. The engineer will collaborate with engineering, analytics, and business teams in an Agile delivery model. The position involves developing and operating batch and near-real-time pipelines, creating Snowflake data models, and maintaining ETL/ELT workflows. It also includes scheduling and monitoring jobs, ensuring data quality and governance, tuning performance and cost, enabling data products and AI-ready datasets, participating in Agile and SDLC processes, and resolving production issues. Ally describes its work arrangements as fully on-site, hybrid, or fully remote. The specific work requirements for this role are discussed during hiring and may change based on leadership discretion or business needs. The position offers a base pay range of $85,000 to $150,000 and eligibility for an annual incentive plan.

Requirements

Strong expertise in Snowflake architecture, SQL development, performance tuning, security and roles, data loading and unloading, and best practices.
Hands-on experience with the AWS data ecosystem, commonly including S3, IAM, and CloudWatch, and potentially Glue, Lambda, EC2, Step Functions, EMR, or Kinesis.
Strong Python programming for data engineering, including ETL/ELT frameworks, API ingestion, automation, unit testing, and logging.
Experience designing and managing enterprise job scheduling with Control-M, including dependencies, calendars, SLAs, monitoring, and incident handling.
Advanced SQL skills for transformations, optimization, and data validation across large datasets.
Experience with Git and CI/CD practices for data pipelines.
Monitoring, alerting, and production support experience in a 24x7 or business-critical environment.
Strong experience working in Agile/Scrum teams and delivering within structured SDLC processes.
Excellent technical and non-technical communication skills, including explaining complex data concepts clearly.
Ability to coordinate across Data Engineering, Data Science, DevOps, Security, BI, and business stakeholders.
Strong ownership mindset, problem-solving ability, and attention to detail.
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
At least 3 years of experience, as stated under Minimum qualifications.
The Qualifications (Typical) section states 9+ years of data engineering experience, including enterprise-grade data platform delivery and production support.

Nice To Haves

Experience enabling AI/ML pipelines or feature datasets; familiarity with ML lifecycle concepts, feature engineering, or MLOps tools and processes.
Solid experience building and maintaining IBM DataStage jobs, handling complex transformations, and supporting production workloads.
Experience with data governance or metadata tools and practices, including catalog, lineage, and data quality frameworks.
Exposure to streaming or event-driven architectures.

Responsibilities

Design, develop, and maintain end-to-end batch and near-real-time data pipelines using Snowflake, AWS services, and Python.
Build and optimize Snowflake data models, including dimensional models, data vaults, or curated data marts.
Develop and maintain Python and IBM DataStage ETL/ELT workflows, and migrate or modernize workloads where applicable.
Implement job scheduling, monitoring, and operational support using Control-M, including alerting, retries, SLAs, and dependency management.
Ensure data quality, governance, lineage, and documentation standards across pipelines.
Tune performance and optimize costs across Snowflake and AWS, including query optimization, clustering, warehouse sizing, and storage management.
Partner with Data Science/AI, BI, Product, and Platform stakeholders to enable data products and AI-ready datasets.
Participate in Agile ceremonies and contribute to estimation, planning, and sprint execution; follow SDLC and change management processes.
Troubleshoot production issues, perform root-cause analysis, and drive preventative improvements.

Benefits

Market-competitive base pay and pay-for-performance incentives based on personal and company goals.
At least 20 paid time off days, 11 paid holidays, and 8 hours of volunteer time off yearly; amounts are prorated based on start date and programs vary by employment status and management level.
401(k) retirement savings plan with matching and company contributions.
Student loan pay-down and 529 educational savings assistance programs.
Tuition reimbursement.
Employee stock purchase plan.
Financial learning center and financial coach access.
Flexible medical, dental, and vision insurance options.
Employee, spouse, and child life insurance.
Short- and long-term disability insurance.
Health Savings Account with employer contributions and Healthcare FSA.
Critical illness, accident, and hospital indemnity insurance.
Total well-being program supporting physical, social, emotional, and financial well-being.
Adoption, surrogacy, and fertility assistance.
Paid parental and caregiver leave.
Dependent Day Care FSA, backup child and adult/elder care days, and childcare discounts.
Employee Assistance Program.
Subsidized and discounted Weight Watchers program and other employee discount programs.
Depending on the role, travel allowances, relocation assistance, a signing bonus, and/or equity.
Annual incentive plan eligibility.