Staff Software Engineer, Data Platform

Credit Acceptance is an award-winning company recognized for its workplace culture and commitment to success. They are seeking a Staff Software Engineer to own the architecture and implementation of key components of their modern data platform while driving technical innovation and collaborating with engineering teams to enhance platform capabilities.

Responsibilities:

• Own architecture and implementation of key components of the modern data platform (e.g., data lake, streaming infrastructure, DaaS, DAL, data catalog), ensuring production reliability and technical soundness
• Drive technical innovation by contributing to system design, implementation, and operational excellence in high-impact areas of the platform
• Model strong engineering practices through hands-on work and code contributions, demonstrating how engineers should approach problems and uphold quality
• Collaborate with peers across data and engineering teams to influence technology and architecture decisions, providing well-reasoned perspectives
• Advocate for adoption of new technologies and demonstrate their value through prototypes, proofs of concept, and integration into team workflows
• Align project execution with broader strategies by working with senior engineers and engineering leadership to support the company’s technical and business direction
• Conduct impact analysis to proactively identify impact of a change across services and systems
• Evaluate third-party technologies and solutions through technical assessments and provide recommendations that balance technical fit with business needs
• Experiment and validate ideas by testing assumptions, analyzing results, and recommending practical solutions to improve platform capabilities
• Contribute to documentation of standards and best practices, making platform engineering approaches clear and maintainable for other teams
• Debug and resolve complex production issues, applying technical expertise to restore stability across services and systems
• Participate in continuous learning and improvement efforts, helping refine processes, design practices, and team workflows for better engineering outcomes
• Grow talent by participating in hiring and mentoring team members

Requirements:

• Bachelor's degree in Computer Science, Information Systems, or a closely related field; or equivalent work experience
• Minimum 5 years of software engineering experience, with recent hands-on experience building and maintaining data platforms or distributed systems in cloud environments
• Strong knowledge of software engineering best practices, with practical experience building and operating data platforms, products, or solutions
• Experience developing and supporting cloud-native applications (AWS, Azure, or GCP), including containerized services (Docker, Kubernetes, ECS/EKS)
• Working knowledge of lakehouse technologies (Delta Lake, Iceberg, Hudi) with hands-on experience in schema evolution and optimization
• Strong understanding of observability practices (metrics, logging, tracing, alerting) and experience applying them with tools such as Dynatrace, Splunk, or CloudWatch to ensure platform reliability and performance
• Applied experience with data storage and processing technologies, including object stores (S3, ADLS, GCS), relational databases, and NoSQL systems
• Awareness of data governance and security practices (e.g., access controls, encryption, compliance considerations), with the ability to design platform components that align with organizational standards
• Strong knowledge of distributed systems concepts (scalability, reliability, consistency, partitioning) and their application to large-scale data platforms
• Experience working with enterprise-class applications where uptime, reliability, and scalability are essential
• Strong programming skills in one or more languages commonly used for platform engineering (e.g., Python, Java, Scala, Go)
• Demonstrated ability to mentor and coach less experienced engineers, contributing to team growth and technical maturity
• Familiarity with Agile delivery practices and other software development lifecycle methodologies
• Advanced expertise in lakehouse technologies (Delta, Iceberg, Hudi), including performance tuning and reliability at scale
• Hands-on experience with workflow orchestration frameworks (Airflow, Dagster, Prefect, Databricks Workflows)
• Strong background in CI/CD pipelines for platform services
• Deep familiarity with observability and SRE practices (SLAs/SLOs/SLIs, distributed tracing, advanced monitoring tools)
• Experience with performance tuning and cost optimization for large-scale data platforms
• Financial services or FinTech industry experience