Staff Data Engineer

The role:

You will play an important role in developing data pipelines, frameworks, and models to support the understanding of our users and better product decisions. You will help empower product teams with a complete self-serve analytics platform by working on scalable, robust solutions while collaborating with data engineers, data scientists, and data analysts across the company.

What you’ll do:

• Drive Architectural Vision & Execution

  • Design for Scale: Lead the architectural design and hands-on implementation of high-throughput, low-latency data pipelines using Spark and Kafka to process massive datasets.
  • Build Core Infrastructure: Develop, optimize, and maintain robust data models in BigQuery and orchestrate complex, multi-layered data workflows using Apache Airflow.
  • Operationalize AI/ML: Partner directly with Data Science teams to take machine learning models out of the lab and into production. Design the architecture that allows these models to serve real-time predictions and insights to downstream services.

• Lead Complex, Cross-Functional Initiatives

  • Navigate Ambiguity: Take ownership of highly ambiguous business problems, translating vague stakeholder requirements into concrete, actionable technical roadmaps.
  • End-to-End Delivery: Autonomously drive complex, multi-squad projects from the initial whiteboard design phase through large-scale deployment and long-term maintenance.
  • Cross-Business Alignment: Collaborate with Product Owners and technical leaders across different Business Units to ensure your data systems support the overall company strategy and maximize end-user value.

• Elevate Engineering Standards & Team Performance

  • Champion Quality: Actively lead large-scale refactoring efforts and continuously drive improvements in code quality, system reliability, and internal tooling.
  • Proactive Problem Solving: Act as the vanguard for system health—spotting subtle, long-term performance bottlenecks early and architecting workable solutions before they impact the business.
  • Mentorship & Coaching: Dedicate time to leveling up the broader engineering organization. Conduct rigorous architectural and peer code reviews, mentor senior and junior engineers, and promote a pervasive culture of technical excellence.

What you bring in:

• Architecture & Tech Stack

  • Core Engineering: Exceptional proficiency in programming in Python / Scala to build robust, highly optimized data systems.
  • Distributed Systems: Extensive architectural experience with Apache Spark and Kafka (or equivalents like Flink, Kinesis, GCP Pub/Sub) to build high-throughput, low-latency pipelines handling AdTech-scale data (millions of events/sec).
  • Data Warehousing & Orchestration: Expert in data modeling and query optimization in BigQuery (or Snowflake / Redshift). Proficient in orchestrating complex DAGs and workflows using Apache Airflow (or Dagster / Prefect).
  • MLOps & AI Integration: Experience deploying machine learning models into production. Ability to engineer deployment architectures that translate AI models into scalable, low-latency APIs for downstream services.

• Complex Execution

  • End-to-End Ownership: Proven track record of leading complex, cross-squad projects from initial whiteboard design through large-scale deployment and maintenance.
  • Problem Decomposition: Strong ability to break down highly ambiguous technical problems into clear, actionable development plans for multiple teams.

• Engineering Leadership

  • Engineering Standards: Drives the adoption of architectural best practices, robust testing methodologies, and high engineering standards across the organization.
  • System Evolution: Leads large-scale codebase refactoring, system health improvements, and the evolution of internal tooling.
  • Mentorship: Actively mentors senior and junior engineers, conducts rigorous architectural/code reviews, and guides the team’s long-term technology choices.

It would be great if you also have:

• Transactional Databases: Hands-on experience with operational RDBMS (e.g., PostgreSQL, MySQL) and low-latency NoSQL data stores (e.g., Redis, Cassandra).
• Microservices Architecture: Experience building or integrating with microservice ecosystems, including API design (REST, gRPC) and container orchestration (Docker, Kubernetes).
• Infrastructure as Code (IaC): Proficiency in provisioning cloud data infrastructure using Terraform and configuring robust CI/CD pipeline