MastercardLead Data Engineer
Mastercard is hiring a Lead 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.
- The posting mentions Databricks, AI agents and AI automation among its tools and skills.
- Level classes it as a lead role.
- Mastercard has 1,000+ other open roles on Level.
AI in this role
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
Lead Data EngineerPosition OverviewHave you ever wanted to be part of something BIG?
Now is the time to make an immediate impact at a leading global technology company, Mastercard.
This role is part of the AI&DPE Data Engineering platform team, responsible for building and evolving large scale backend data systems, real time and batch pipelines, and AI enabled services that power analytics, decisioning, and automation across the organization.
This is a backend engineering role, focused on Scala/Python/Java, distributed data platforms (Cloudera/Spark), and cloud based architectures, with a strong emphasis on AI agent creation and intelligent automation. Visualization tools (e.g., Qlik) are consumers of the platform, not the core focus of this role.
You will work with massive transactional datasets, modern big data and cloud platforms, and AI driven workflows to transform how Mastercard processes, enriches, and operationalizes data at global scale.
PRIMARY RESPONSIBILITIES
Backend & Data Platform Engineering
Design, develop, and maintain backend services and data pipelines using Scala, Python and Java
Build and optimize batch and streaming workloads on Cloudera Data Platform (CDP) using Spark
Work with Cloudera Manager to support platform configuration, monitoring, performance tuning, and operational stability
Design and implement high quality datamarts and curated datasets with strong emphasis on data integrity, performance, and reliability
AI Agents & Intelligent Automation
Design, build, and integrate AI powered agents that operate to support:
Anomaly detection and operational intelligence
workflow automation
Apply generative AI driven, or rule based agents to reduce manual effort and improve scalability across backend systems
Cloud & Modern Data Architecture
Build and support data and compute workloads in AWS environments
Leverage Databricks for large scale data processing, advanced analytics
Contribute to cloud native and hybrid architectures integrating on prem and cloud platforms
Integration & Downstream Enablement
Enable downstream consumers (analytics, visualization, reporting tools such as Qlik) through well designed, reliable backend data interfaces
Partner with analytics, fraud, and business teams to ensure backend systems meet evolving needs without compromising platform stability
Documentation & Collaboration
Create clear technical documentation, including architecture diagrams, data flows, and design specifications
Participate in Agile/Scrum ceremonies and cross functional design reviews
Mentor and upskill team members in backend engineering, big data, and AI agent concepts
KNOWLEDGE AND SKILL REQUIREMENTS
Required
BS/BA degree in Computer Science, Engineering, Information Systems, or related field
Strong handson experience with Scala,Python/Java in backend or data intensive systems
Experience working with Cloudera Data Platform (CDP) and Spark
Familiarity with Cloudera Manager for cluster administration, monitoring, or troubleshooting
Strong understanding of data modeling concepts, distributed systems, and large scale data processing
Excellent problem solving skills and ability to work independently in complex environments
GOOD TO HAVE / STRONGLY PREFERRED
Hands on experience building, integrating, or supporting AI driven agents or intelligent automation solutions
Experience with Databricks for data engineering or ML workloads
Experience working in AWS (e.g., S3, EC2, EMR, Glue, Lambda, IAM, or equivalent services)
Knowledge of streaming and big‑data technologies:
Kafka
Hadoop ecosystem
Hive/Impala
Exposure to model monitoring, or AI platform enablement
Experience with ETL tools such as Informatica
Experience working in Agile / Scrum teams within large enterprises
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
Lead Data Engineer at Mastercard rates 63 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
Also common in data engineer jobs at this level
Pay
- Typical data engineer job
- $149k
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 decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a workflow you automated with AI tools, end to end.
- What are the limits of Databricks that you've run into, and how did you work around them?
- 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: AI agents, AI Automation, 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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