Description

You will design, build, and operate production-grade data pipelines and platforms at scale.

This role is remote.

Responsibilities

• Lead the end-to-end delivery of scalable batch and real-time data pipelines, ensuring reliability, security, and performance.
• Design and optimize Airflow DAGs to improve maintainability and operational efficiency across enterprise analytics workloads.
• Partner with Finance, Marketing, and Enterprise Analytics teams to translate business requirements into trusted data solutions.
• Implement CI/CD, test automation, and observability practices to drive engineering excellence and reduce technical debt.
• Manage incident response, SEV handling, and root cause analysis while maintaining dashboards and runbooks for production support.

Required Skills

• 10+ years of experience in Data Engineering and Data Platforms with ownership of production systems.
• Hands-on expertise with Databricks, Snowflake, and AWS cloud infrastructure.
• Strong proficiency in Python and SQL, with a deep understanding of software engineering best practices.
• Extensive experience with big data technologies including Spark, Trino, Hive, and cloud storage systems.
• Experience with orchestration frameworks such as Airflow, including DAG optimization and performance tuning.
• Strong understanding of data quality, governance, security, lineage, and self-service analytics architectures.
• Experience with semantic models, semantic layer tools (e.g., Cube), and BI platforms (e.g., ThoughtSpot).
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.

Preferred Skills

• Exposure to graph databases, vector databases, conversational analytics, or agentic AI applications.
• Experience in Transportation, MobilityTech, Ride-sharing, Marketplace, Logistics, or large-scale Consumer Technology environments.

Key Skills

Education

Bachelor's degree