Responsibilities
- Architect, design, lead and build end-to-end performant, reliable, scalable data platform.
- Be an independent individual contributor who can solve problems and deliver high-quality solutions without oversight and a high level of ownership.
- Mentor, guide and work with junior engineers to deliver complex and next-generation of features.
- Bring a customer-centric, product-oriented mindset to the table - collaborate with customers and internal stakeholders to resolve product ambiguities and ship impactful features.
- Partner with engineering, product, design, and other stakeholders in designing and architecting new features.
- Experimentation mindset - autonomy and empowerment to validate a customer need, get team buy-in, and ship a rapid MVP.
- Quality mindset - you insist on quality as a critical pillar of your software deliverables.
- Analytical mindset - instrument and deploy new product experiments with a data-driven approach.
- Monitor, investigate, triage, and resolve production issues as they arise for services owned by the team.
- Create and maintain data pipelines and foundational datasets to support product/business needs.
- Design and build database architectures with massive and complex data, balancing with computational load and cost.
- Develop audits for data quality at scale, implementing alerting as necessary.
- Create scalable dashboards and reports to support business objectives and enable data-driven decision-making.
- Troubleshoot and resolve complex issues in production environments.
Requirements
- 10+ years of designing, implementing and delivering highly scalable and performant data platform.
- Experience building large-scale (100s of Terabytes and Petabytes) data processing pipelines - batch and stream.
- Experience with ETL/ELT, stream and batch processing of data at scale.
- Expert level proficiency in PySpark, Python, and SQL.
- Expertise in data modeling, relational databases, NoSQL (such as MongoDB) data stores.
- Experience with big data technologies such as Kafka, Spark, Iceberg, Datalake, and 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.
- Experience/knowledge of data processing platforms such as Databricks or Snowflake.
Nice to Have
- An understanding of Graph and Vector data stores.
Benefits
- A fast-paced and collaborative environment
- Learning and development allowance
- Competitive cash and equity compensation and opportunity for advancement
- 100% medical, dental, and vision coverage
- Up to $25K reimbursement for fertility, adoption, and parental planning services
- Flexible PTO policy
- Monthly wellness stipend, home office stipend
- In-office perks are provided, such as lunch four times a week, a commuter stipend, and an abundance of snacks and beverages.
Work Arrangement
Hybrid
Team
Structure: Data Platform team
Additional Information
- One of Checkr’s core values is Transparency. To live by that value, we’ve made the decision to disclose salary ranges in all of our job postings. We use geographic cost of labor as an input to develop ranges for our roles and as such, each location where we hire may have a different range. If this role is remote, we have listed the top to the bottom of the possible range, but we will specify the target range for an exact location when you are selected for a recruiting discussion. For more information on our compensation philosophy, see our website.
- Checkr is committed to building the best product and company, which requires hiring talented and qualified individuals with a diverse set of perspectives and lived experiences. Checkr believes in hiring people of all backgrounds, including those whose histories are impacted by the justice system in accordance with local, state, and/or federal laws, including the San Francisco’s Fair Chance Ordinance.
