19 hours ago

Senior Data Engineer

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 19 hours agoStatus: Live

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

Glassdoor: 4.1

Website LinkedIn Glassdoor

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Mastercard

19 hours ago

Senior Data Engineer

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 19 hours agoStatus: Live

Job Summary

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

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

Glassdoor: 4.1

Website LinkedIn Glassdoor