Senior Data Engineer

Mastercard is hiring a Senior Data Engineer in Pune, India. Level rates it ; you can apply on Mastercard.

Level's read on this job

Level rates this data engineer role Works on AI ●●●○: the daily work is on AI products, without building the model.
Tools and skills the posting cites include Databricks and MLOps.
Level classifies it as a senior role.
Level lists 1,100+ other open roles at Mastercard.
The role was posted in the last few days.

AI in this role

databricks

ml-ops

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, simple, 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

Overview
The Data Engineering team builds trusted, scalable datasets and pipelines that power analytics, BI, and machine learning use cases across Mastercard. This role will help design and deliver resilient data pipelines, curated data models, and ML-ready feature datasets while partnering closely with platform engineering, data science, and BI stakeholders.
Role
As a Data Engineer / MLE, you will:
• Design, build, and maintain scalable batch and near-real-time data pipelines.
• Develop curated datasets and data models that enable reliable analytics and ML workloads.
• Implement pipeline reliability patterns (monitoring, alerting, retries, performance tuning).
• Partner with data scientists to enable feature engineering and model operationalization.
• Ensure secure data handling aligned to enterprise governance and compliance expectations.
• Drive engineering best practices (code quality, testing, documentation, CI/CD where applicable).
All About You
Essential Skills (Must Have):
• Strong proficiency in SQL and Python (or Scala) for data engineering.
• Experience building data pipelines using distributed processing (e.g., Spark,PySpark,HADOOP).
• Experience in using data technology technology like AWS, S3, Databricks.
• Solid understanding of data modeling (dimensional and/or lakehouse patterns) and data quality concepts.
• Experience working with cloud data ecosystems (AWS preferred) and object storage / lake patterns.
• Ability to troubleshoot performance and reliability issues in production pipelines.
• Strong communication skills and ability to collaborate across engineering and analytics stakeholders.
Nice to have:
• Experience with Databricks or similar lakehouse platforms.
• Exposure to ML feature pipelines, model deployment patterns, or MLOps concepts.
• Experience in payments / banking / regulated environments.
• Familiarity with data governance tooling (catalog, lineage) and PII handling patterns.

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

Abide by Mastercard’s security policies and practices;
Ensure the confidentiality and integrity of the information being accessed;
Report any suspected information security violation or breach, and
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

How we rate this

Senior Data Engineer at Mastercard rates 64 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Data Engineer is an open job at Mastercard.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in Pune, India.
The job was posted on October 22, 2026.
Seniority: senior.
Applications are handled by Mastercard. Apply on Mastercard

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsDatabricks

Also common in data engineer jobs at this level

Airflow Azure Snowflake Google Cloud Generative AI Kafka

Pay

Typical data engineer job: $150k

This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used Databricks in your day-to-day work.
Describe a typical day in a role like this one: which parts run through AI directly?
If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

List these exact terms on your resume: ML Ops and Databricks. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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