Senior Data Platform Engineer
On-siteGeneva, Illinois, United States
Job Summary
Drive end-to-end data engineering initiatives from problem discovery through implementation, rollout, and long-term ownership. Design, build, and operate reliable, scalable data pipelines supporting analytics, product insights, and business-critical reporting while ensuring data accuracy and observability. Collaborate with engineering, product, and analytics teams to translate needs into pragmatic technical solutions, improve platform reliability through monitoring and incident response, and lead architecture decisions on data modeling and warehouse practices. Mentor engineers via code reviews and technical guidance, promote strong engineering practices, and take ownership of ambiguous cross-functional problems. Master's degree and 5+ years in data engineering required; Scala, Java, and Python expertise essential.
Required Qualifications
- Master's degree in Computer Science, Engineering, Data Engineering, or a related technical discipline, or equivalent practical experience
- 5+ years of relevant professional experience in data engineering, data platform engineering, backend engineering, distributed systems, or a closely related field
- Strong software engineering skills, with excellent knowledge of Scala and/or Java, as well as solid Python experience
- Experience writing production-grade, maintainable, and well-tested code
- Strong understanding of distributed systems, including scalability, fault tolerance, consistency trade-offs, resource management, and operational failure modes
- Solid experience designing, building, and operating data pipelines in production environments, including batch and/or streaming workloads
- Strong knowledge of databases, SQL, query optimization, data modeling, and data access patterns
- Good understanding of data warehouse and data lakehouse best practices in Big Data environments, including partitioning, schema evolution, data quality, reliability, observability, and performance considerations
- Experience working with large datasets, with the ability to reason about scalability, performance, cost, reliability, and operational trade-offs
- Ability to drive technical initiatives end-to-end, from problem framing and solution design to implementation, rollout, documentation, and long-term ownership
- Comfortable working with ambiguity and translating broad business or platform needs into pragmatic technical plans
- Strong communication skills, with experience collaborating across engineering, analytics, product, and business teams
- Mentoring experience, including supporting other engineers through technical guidance, code reviews, design discussions, and knowledge sharing
- A strong ownership mindset, with the ability to identify problems, propose solutions, align stakeholders, and move initiatives forward without requiring constant direction
Desired Qualifications
- Experience with Apache Spark is highly recommended, especially in large-scale data processing environments
- Experience with Kubernetes and containerized workloads in production environments
- Experience with workflow orchestration, CI/CD, infrastructure-as-code, or cloud-native data platform tooling
- Experience with streaming technologies such as Kafka, Flink, Spark Structured Streaming, or similar
- Experience with observability tooling, monitoring, alerting, incident response, and operational excellence practices
- Experience building internal platforms, developer tooling, self-service data products, or shared infrastructure used by multiple teams
- Familiarity with ClickHouse, Trino, Iceberg, Delta Lake, dbt, Airflow, Argo Workflows, or similar technologies
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.