Senior Data Engineer

Mastercard
India
Workplace: OnsiteFull timeFunction: Solutions Engineering & Sales EngineeringExperience: 6+ yearsSkills: ["Hands-on engineering","Problem-solving","Responsible AI","Governance","Observability"]

Drive AI-led transformation across analytics and data platforms by designing and implementing GenAI/LLM and agentic AI workflows, including multi-agent orchestration, RAG, embeddings, and evaluation. Build production Python services and REST APIs (Flask/FastAPI), modernize codebases with PySpark on Databricks, and architect scalable ETL pipelines with AWS Glue/S3/IAM. Provide expert Databricks and Snowflake support, and apply DBA fundamentals across AWS databases.

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FursaFursa
Mastercard
Mastercard
1 month ago

Senior Data Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Status: Live
Reposted: similar role first listed 6 months ago

Job Summary

Drive AI-led transformation across analytics and data platforms by designing and implementing GenAI/LLM and agentic AI workflows, including multi-agent orchestration, RAG, embeddings, and evaluation. Build production Python services and REST APIs (Flask/FastAPI), modernize codebases with PySpark on Databricks, and architect scalable ETL pipelines with AWS Glue/S3/IAM. Provide expert Databricks and Snowflake support, and apply DBA fundamentals across AWS databases.
Location: India
Workplace: Onsite
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering
Seniority: Mid level

Key Responsibilities

  • •Ideate, design, and implement agentic AI and GenAI/LLM workflows, including multi-agent orchestration and end-to-end LLM-powered pipelines (prompting, chaining, tool use, evaluation).
  • •Design and maintain data pipelines for AI workloads (training, fine-tuning, inference; vector storage; RAG; embeddings) with governance, observability, and cost control.
  • •Develop production-grade Python applications and expose REST APIs using Flask or FastAPI for model inference, AI services, and data access.
  • •Migrate and implement legacy logic/scripts using PySpark on Databricks, following modular architecture, logging/monitoring, exception handling, and unit/integration testing best practices.
  • •Architect and implement scalable ETL pipelines using AWS Glue and AWS services (S3, IAM), ensuring reliable orchestration, monitoring, and error handling; apply DBA fundamentals across AWS databases.

Key Requirements

  • •6+ years of experience in Data Engineering / Analytics Engineering roles.
  • •Strong hands-on experience with GenAI, LLMs, agentic AI, and multi-agent orchestration.
  • •Advanced Python development experience, including Flask or FastAPI for REST APIs.
  • •Strong experience in PySpark and distributed data processing.
  • •Expert-level proficiency with Databricks and Snowflake, plus deep ETL architecture with AWS Glue and cloud-native pipelines.
Experience:6+ yearsAI/GenAIData engineeringAnalytics engineeringETLCloud data platforms
Skills:Hands-on engineeringProblem-solvingResponsible AIGovernanceObservability
Tech Stack:PythonPySparkFlaskFastAPIREST APIsDatabricksSnowflakeAWS GlueAmazon S3IAMAWS BedrockAWS SageMakerAWS LambdaDatabricks AIMicrosoft CopilotAmazon RDSMySQLPostgreSQLMongoDB AtlasMongoDB

Company Brief

Mastercard
Mastercard is a global payments and technology company that processes transactions, provides fraud prevention and data services, and builds payment infrastructure for consumers, businesses, merchants and governments across 200+ countries.
Industry: Payments
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Purchase, New York, United States
Founded: 1966
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Glassdoor: 4.1
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