Our Purpose

- Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, easy, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential._

Title and Summary

Senior Data Engineer

Role: Senior Data Engineer

Role Overview

We are seeking a Senior Data Engineer to drive AI-led transformation across our analytics and data platforms. This role is designed for a hands-on engineer who can ideate, design, and implement AI-driven solutions, modernize Python and data pipelines, and maintain best-in-class analytics environments.

Skill Priority & Core Responsibilities

1️⃣ AI - Agentic Systems, GenAI & Intelligent Workflow Automation

Lead AI ideation by identifying opportunities to eliminate manual effort, reduce operational friction, and improve decision-making.

Design and implement agentic AI solutions, including: Multi-agent orchestration for task delegation and workflow execution

AI agents for monitoring, diagnostics, reconciliation, and data reasoning

Build end-to-end GenAI / LLM-powered workflows, including prompt engineering, chaining, tool use, and evaluation.

Design and maintain data pipelines specifically for AI workloads, supporting: Training, fine-tuning, and inference

Vector storage, retrieval-augmented generation (RAG), and embeddings

Integrate AI solutions with existing data platforms while ensuring governance, observability, and cost control.

2️⃣ Python Engineering, APIs & PySpark Development





Design and expose REST APIs using Flask or FastAPI for model inference, AI services, and data access.

Improve and refactor existing Python codebases for maintainability, performance, and scalability.

Migrate legacy logic and scripts into PySpark-based implementations on Databricks.

Follow best practices in: Modular architecture and design patterns

Logging, monitoring, and exception handling

Unit and integration testing

3️⃣ Analytics Platforms - Databricks & Snowflake Expertise

Act as an expert practitioner for Databricks and Snowflake, supporting both analytics and AI-driven workloads.

Perform advanced problem solving related to: Performance tuning

Cost optimization

Query optimization and data layout

Define and enforce standards and best practices for analytics and AI workloads on these platforms.

Support administration and daily management activities, including: Workspace and resource governance

User access and role management

Platform usage optimization

Enable analytics teams through reusable patterns, templates, and documentation.

4️⃣ ETL & Cloud Data Engineering (AWS)

Architect and implement scalable ETL pipelines using modern cloud-native patterns.

Build and maintain pipelines using AWS Glue, interacting with S3, IAM, and related AWS services.

Ensure reliable orchestration, monitoring, and error handling across data pipelines.

Optimize ETL workloads for performance, scalability, and cost efficiency.





5️⃣ DBA Concepts & Data Storage Expertise

Work with relational and NoSQL databases, including: Amazon RDS (MySQL, PostgreSQL)

MongoDB Atlas

Indexing and query optimization

Backup, recovery, and high availability

Capacity planning and performance troubleshooting

Collaborate with infrastructure and database teams on design and operational improvements.

Cloud-Based AI Services & Platforms (Cross-Cutting Skill Area)

Hands-on experience or strong familiarity with cloud AI and compute services, including: AWS SageMaker for training, experimentation, and deployment

AWS Lambda for event-driven AI and data automation

Databricks AI for ML/GenAI workloads and platform integration

Microsoft Copilot and related AI tooling for productivity enablement and integration scenarios

Evaluate, integrate, and operationalize AI services based on cost, security, scalability, and use-case fit.

Must‑Have Skills

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.

Strong experience in PySpark and distributed data processing.

Expert-level proficiency with Databricks and Snowflake.

Deep understanding of ETL architecture, AWS Glue, and cloud-native data pipelines.

Good‑to‑Have Skills

Experience with vector databases and RAG architectures.

Exposure to ML lifecycle management, MLOps, and experiment tracking.

Infrastructure-as-Code (Terraform or similar).

Working knowledge of DBA concepts and databases on AWS.

Experience working in regulated domains (banking, fintech, healthcare).

CI/CD practices for data, AI, and analytics pipelines.

📌 Senior Data Engineer (Navi Mumbai)
🏢 Mastercard
📍 Navi Mumbai