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Mastercard India Services Pvt Ltd
Data Engineering Manager
Mastercard India Services Pvt Ltd
Pune•On-site•10-13 Yrs•Jul 6
Job description
Data Engineering ManagerPlatform EngineeringAI EnablementDistributed Systems
Overview
Manager, Data Engineering leading the strategy, design, and operation of enterprise-scale data platform that powers analytics, applications, and AI-enabled use cases across the organization.
Responsibilities
- Own the end-to-end vision, roadmap, and architecture for the enterprise data platform
- Provide technical and organizational leadership over scalable data pipelines using technologies such as Apache NiFi, Airflow, Spark (batch/streaming)
- Ensure consistent design and governance of data ingestion, transformation, enrichment, and access patterns
- Define and govern data schemas, contracts, and transformations ensuring data quality, consistency, and backward compatibility
- Drive platform performance, scalability, reliability, and cost optimization
- Establish platform-wide data quality standards, monitoring, alerting, and SLAs for critical data assets
- Oversee use of object storage platforms (MinIO/Ceph/S3-compatible APIs) including data layout, lifecycle management, and retention policies
- Own operational readiness for batch and near-real-time processing including incident management and root cause analysis
- Provide architectural oversight for containerized data workloads and services deployed on Kubernetes-based platforms
- Partner closely with DevOps, SRE, and infrastructure teams to ensure observability, resiliency, and operational maturity
- Guide CI/CD, automation, and infrastructure-as-code practices for data platform components
- Lead platform modernization efforts, capacity planning, and preparation for hybrid or public cloud adoption
- Partner with AI/ML teams to ensure the data platform supports AI-driven use cases
- Define patterns for integrating AI-enabled capabilities into enterprise data workflows
- Ensure compliance with enterprise security, privacy, and governance requirements
- Act as a senior partner to application teams, analytics teams, AI teams, and product leaders
- Communicate platform strategy, risks, and trade-offs clearly to executive and senior stakeholder audiences
- Establish documentation, standards, and best practices to support self-service and platform adoption
- Build, mentor, and lead experienced engineers
Qualifications
- Strong experience in data engineering, platform engineering, or distributed systems
- Experience leading enterprise-scale data engineering teams
- Proven track record owning and operating mission-critical data platforms in production environments
- Strong architectural understanding of enterprise data platforms and distributed data systems
- Hands-on background with Apache Spark (batch/streaming), Apache NiFi, Apach Airflow
- Experience with object storage systems (S3-compatible storage)
- Experience operating data workloads on Kubernetes-based platforms
- Strong understanding of data modeling, schema evolution, pipeline design, and reliability patterns
- Exposure to public cloud platforms (AWS, Azure, or GCP) and hybrid deployment models
- Ability to apply cloud-native design principles to guide platform modernization and migration strategies
Nice to have
- Experience with Kafka or event-driven architectures
- Familiarity with lakehouse technologies (Parquet, Delta Lake, Iceberg, or Hudi)
- Experience enabling AI/ML use cases through data platforms (not model development)
- Exposure to monitoring and observability stacks (e.g., Prometheus, Grafana, ELK)
- Background in regulated or security-conscious environments (e.g., financial services)