Why This Job is Featured on The SaaS Jobs
Senior data engineering roles are increasingly central to modern SaaS, where product decisions, customer experiences, and risk controls are mediated through data platforms. This position stands out for its emphasis on building both batch and streaming pipelines on AWS, a common backbone for SaaS companies consolidating analytics, ML use cases, and operational reporting into a single, governed layer.
From a career perspective, the work maps closely to the skills that remain portable across SaaS businesses: designing resilient pipelines, modeling data for multiple consumers, and treating reliability and observability as first-class engineering concerns. The remit also signals meaningful cross-functional exposure, translating ambiguous product and analytics needs into production-grade datasets and services, which is often where seniority in SaaS data teams is demonstrated.
The role is best suited to an engineer who prefers hands-on ownership of complex delivery while contributing to architectural direction without being the long-term platform strategist. It will fit someone comfortable debugging production issues, optimizing performance, and collaborating with product and analytics partners to define what “good data” means in practice.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Role
We are seeking a strong Senior Data Engineer to build and maintain scalable, high-quality data pipelines powering Checkr’s centralized data platform. As a Senior Engineer, you will independently deliver complex features, contribute to system design, and collaborate with cross-functional partners to support the next generation of our data products.
What You’ll Do
- Independently design and implement complex batch and streaming pipelines using PySpark, SQL, and AWS services.
- Navigate ambiguity with guidance, translating high-level direction into well-scoped, high-quality technical solutions.
- Work cross-functionally with product, design, analysts, and engineers to ship impactful features and improve data workflows.
- Contribute to architectural discussions and system improvements without owning long-term strategy.
- Ensure pipeline reliability and data quality, implementing testing, monitoring, and observability best practices.
- Investigate and resolve production issues for services owned by the team.
- Write performant, maintainable code that aligns with engineering standards.
- Support the team in building foundational datasets that enable analytics, ML, and customer-facing features.
What You Bring
- 6–7+ years of experience in data engineering with strong hands-on execution ability.
- Proficiency with PySpark, Python, and SQL, including debugging and performance optimization.
- Experience building large-scale pipelines (up to terabytes or larger), with exposure to streaming systems such as Kafka.
- Strong knowledge of data modeling, relational databases, and NoSQL stores.
- Experience with AWS services such as EMR, Glue, Athena, Lambda, and S3.
- Exposure to Iceberg or other lakehouse technologies (nice to have).
- Understanding of security and data privacy fundamentals.
- Strong problem-solving skills, attention to detail, and ability to execute independently.
- Knowledge of Databricks, Snowflake, or Graph/Vector stores is a plus.
