Responsibilities
- 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.
Requirements
- 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.
Team
Structure: People Data team
