Description

• 10+ years of experience in Data Engineering and Data Platforms with demonstrated ownership of production-grade pipelines and systems
• Hands-on expertise with Databricks, Snowflake, and AWS cloud infrastructure
• Strong proficiency in Python and SQL, with deep understanding of software engineering best practices including test automation, CI/CD, and DevOps
• Extensive experience with big data technologies including Spark, Trino, Hive, and cloud storage systems
• Experience building and maintaining scalable batch and real-time data pipelines supporting enterprise analytics workloads
• Hands-on experience with orchestration frameworks such as Airflow and optimizing DAG performance, reliability, and maintainability
• Experience with semantic models, semantic layer tools (e.g., Cube), and BI platforms (e.g., ThoughtSpot)
• Strong understanding of data quality, governance, security, lineage, and self-service analytics architectures
• Experience with incident management, root cause analysis (RCA), production support, on-call rotations, and SEV handling
• Experience with monitoring systems, dashboards, alarms, and runbooks for operational excellence
• Exposure to graph databases, vector databases, conversational analytics, semantic modeling, or agentic AI applications is considered an asset
• Strong analytical, problem-solving, communication, and stakeholder management skills
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
• Experience working in Transportation, MobilityTech, Ride-sharing, Marketplace, Logistics, Delivery, or other large-scale Consumer Technology environments is highly preferred, with exposure to high-volume, real-time data platforms supporting analytics, AI/ML, and operational systems.

Role and Responsibilities

• Lead the design, development, deployment, and operation of critical data systems and pipelines, owning solutions from concept through production delivery
• Build and optimize scalable batch and real-time data pipelines, ensuring reliability, performance, security, and data quality
• Partner closely with Finance, Marketing, People, and Enterprise Analytics teams to align on business requirements and deliver trusted data solutions
• Collaborate with Data Platform teams, Databricks, and semantic layer stakeholders to support migration initiatives and platform modernization efforts
• Design, develop, and optimize Airflow DAGs to improve maintainability, reliability, and operational efficiency
• Participate in on-call rotations, incident response, SEV management, and post-incident root cause analysis, implementing long-term reliability improvements
• Monitor system health and maintain dashboards, alerts, alarms, and runbooks to support production environments
• Design and maintain semantic models and reusable data assets enabling self-service analytics across business functions
• Lead architecture reviews, identify technical debt, and propose scalable solutions to reduce complexity and operational burden
• Create and maintain detailed technical documentation, design specifications, and operational processes to support knowledge sharing and onboarding
• Drive engineering excellence by implementing best practices around CI/CD, testing automation, observability, and data platform reliability
• Collaborate with cross-functional stakeholders and technical teams to deliver high-quality, scalable data solutions supporting enterprise analytics and AI initiatives

Key Skills

Education

Bachelor's degree