Engineering Manager (Data Platform)

Job Profile: Engineering Manager (Data Platform) Location: Bangalore | Karnataka Years of Experience: 8–10 About the Team Swiggy's Data Platform organization sits at the intersection of streaming infrastructure, data warehousing, lakehouse architecture, and data governance. The team owns the foundational systems that power real-time and batch data movement across the company — spanning ingestion pipelines, CDC connectors, orchestration, storage, and access control — enabling every downstream team, from analytics to ML to product engineering, to work with trusted, timely, and well-governed data. The team is expected to go beyond keeping pipelines running and build durable platform capabilities into how data is ingested, processed, stored, secured, and consumed at scale — across systems like Kafka, Databricks, Snowflake, and AWS. This includes driving reliability and performance in streaming and batch workloads, enforcing fine-grained access control and data protection standards, and ensuring the platform scales cost-effectively as data volumes and use cases grow across the business. The Role As a Data Platform Manager, you will lead a team responsible for building and scaling Swiggy's core data infrastructure — spanning streaming pipelines, CDC connectors, lakehouse storage, access control, and cost observability across Kafka, Databricks, Snowflake, and AWS. This is not a generic data engineering role. It requires a leader who can combine deep systems expertise, architectural judgment, and strong execution to turn recurring reliability, scale, and governance challenges into durable platform capabilities. You will operate across multiple modes: driving day-to-day pipeline reliability and incident response, improving the performance and cost-efficiency of streaming and batch workloads, shaping access control and data protection standards across the org, and building automation that prevents classes of data quality, schema, and scaling issues from recurring in production. What You Will Work On Driving streaming and batch data platform reliability, including pipeline health, schema compatibility enforcement, and stronger production-readiness gates for critical data flows. Improving CDC and connector management by moving from manual debugging toward automated monitoring, alerting, and self-healing for Kafka-to-Snowflake and other sink/source connectors. Strengthening data access governance across Unity Catalog, including row-level security, attribute-based access control, and secure data-sharing patterns for sensitive datasets. Building better platform tooling and observability frameworks for cost visibility, capacity planning, and infrastructure/pipeline health with low operational overhead. Leading incident and escalation response for P0/P1 data platform issues, including war-room coordination, stakeholder communication, and closure discipline. Improving the quality of internal data quality validation so downstream analytics and ML issues are caught at the platform layer, not discovered by consuming teams. Partnering with platform, data engineering, and product teams on standards across ingestion, streaming architecture (Spark Structured Streaming, Kafka), warehousing (Snowflake, Databricks), and secure data delivery workflows. Managing and mentoring data platform engineers, setting operating rhythm, and ensuring the team scales through clarity, ownership, and repeatable execution. Own the execution and evolution of Swiggy's data streaming, warehousing, and platform governance programs. Build a high-trust operating model for pipeline reliability, incident response, escalations, and stakeholder communication. Ensure critical data issues are tracked with clear severity, explicit affected scope, accountable owners, and closure criteria. Drive platform reliability into the data lifecycle through automation such as schema validation, connector health checks, cost monitoring, and reusable detection workflows. Define secure and scalable