Staff Data Engineer

As a Staff Data Engineer on the People Data team, you'll help build and evolve the centralized platform that powers every Checkr product. This platform stores and serves the identity and people records that underpin Checkr's Workforce, Mortgage, Tenant, Trust, and Personal products, making it foundational to the company's AI-powered verification platform and every high-stakes decision our customers make.

In this role, you'll own the core services, data pipelines, and architecture that keep this platform scalable, reliable, and ready for the next generation of Checkr products. You'll solve complex distributed systems and data engineering challenges while influencing the technical direction of one of the company's most critical platforms.

What you´ll do:

• Architect, design, lead, and build an end-to-end, performant, reliable, scalable data platform.
• Work as an independent contributor: solve problems and deliver high-quality solutions with minimal oversight and strong ownership.
• Mentor and guide junior engineers to deliver complex, next-generation features.
• Bring a customer-centric, product-oriented mindset.  Collaborate with customers and internal stakeholders to resolve product ambiguities and ship features that solve real customer problems.
• Partner with engineering, product, design, and other stakeholders to design and architect new features.
• Experimentation mindset: autonomy and empowerment to validate a customer need, get team buy-in, and ship a rapid MVP.
• Quality mindset: you treat quality as a non-negotiable part of your software deliverables.
• Analytical mindset: instrument and deploy new product experiments with a data-driven approach.
• Monitor, triage, and resolve production issues for the team's services.
• Create and maintain data pipelines and foundational datasets to support product and business needs.

What You Bring:

• 10+ years designing, implementing, and delivering highly scalable, performant data platforms.
• Experience building large-scale data processing pipelines using ETL/ELT, batch, and stream processing.
• Expert-level proficiency in PySpark, Python, and SQL.
• Expertise in data modeling, relational databases, and NoSQL data stores (e.g., MongoDB).
• Experience with big data technologies such as Kafka, Spark, Iceberg, data lakes, and the AWS stack (EKS, EMR, Serverless, Glue, Athena, S3, etc.).
• Knowledge of security best practices and data privacy concerns.
• Strong problem-solving skills and attention to detail.
• Nice to have: experience or knowledge of data processing platforms such as Databricks or Snowflake.
• An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes.