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Lead Data Engineer

Mastercard
Maharashtra, IND
Full-time
Onsite
Discovered 3 weeks ago
Apache SparkScala or JavaHadoop ecosystemObject storage platformsApache Airflow, Apache NiFi, or similar scheduling frameworksSQL

Full Job Posting

Role overview

Design, build, and scale next-generation data platforms and analytical ecosystems for enterprise data warehousing, lakehouse environments, analytics, BI, and regulatory reporting.

Contribute to large-scale data engineering and cloud transformation supporting global payment processing platforms.

Data engineering and architecture

  • Build secure, scalable, resilient Spark and Java/Scala pipelines across Hadoop and cloud object storage; support batch and near-real-time petabyte-scale workloads.
  • Develop reusable engineering components and portable architectures for on-premises and public cloud deployment.
  • Implement data lineage, metadata management, cataloguing, quality controls, and observability; establish scalable engineering patterns with platform teams.
  • Migrate legacy ETL and warehouse workloads to cloud-native architectures; use services such as S3, EMR, and Glue and advance lakehouse and distributed computing.
  • Lead end-to-end development from requirements and design through coding, testing, deployment, and production support.
  • Mentor junior engineers, coordinate with stakeholders, troubleshoot production incidents, perform root-cause analysis, and implement remediation.
  • Maintain security, quality, and governance standards; improve performance, automation, monitoring, and operational efficiency.
  • Evaluate emerging technologies through proofs of concept and contribute to innovation initiatives.

Required experience

  • 10–12 years delivering enterprise-scale data warehouse, data lake, or lakehouse solutions.
  • Experience delivering multiple end-to-end data engineering projects in large distributed computing environments.
  • Hands-on migration of ETL and analytics workloads from on-premises platforms to cloud-native environments.
  • Strong experience with Spark, Scala or Java, Hadoop ecosystem technologies, and object storage.
  • Experience building orchestration and workflow solutions using Airflow, NiFi, or similar enterprise scheduling frameworks.
  • Strong SQL and experience with relational and NoSQL databases, including examples such as Oracle, SQL Server, Cassandra, and DynamoDB.
  • Working knowledge of cloud platforms; AWS services including S3, EMR, and Glue are preferred.
  • Analytical problem-solving, Agile delivery, communication, distributed-team collaboration, ownership, accountability, and execution skills.

Corporate security responsibilities

Follow Mastercard security policies, protect the confidentiality and integrity of accessed information, report suspected security violations or breaches, and complete mandatory security training.

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