MastercardSEND Continuum - Data WARE HOUSE
Mastercard is hiring an SEND Continuum - Data WARE HOUSE in Pune, India. Level rates it ; you can apply on Mastercard.
Level's read on this job
- Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
- Tools and skills the posting cites include Databricks, Python and Spark.
- Level lists 1,100+ other open roles at Mastercard.
- The role was posted in the last few days.
AI in this role
Design and maintain scalable data pipelines and ML-ready feature datasets to power analytics and machine learning workloads.
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
SEND Continuum - Data WARE HOUSEData Engineer / Machine Learning Engineeroverview-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).
• 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
SEND Continuum - Data WARE HOUSE at Mastercard rates 65 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.
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.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where sql was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: ML Ops, Data Engineering, Machine learning, ETL, and SQL. 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.
Want an expert to read your CV for this job?
Free. Send your CV and the role you want next. We reply by email within 2 to 4 business days.
Get new data jobs (Works on AI ●●●○ or higher) by email
One email a week with the new data jobs (Works on AI ●●●○ or higher), each rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.
Free. One email a week. Unsubscribe in one click.
Similar roles
Data roles that work on AI, at other companies.
What kind of AI work fits you?
Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.
Find my next step