Senior Data Engineer-1

AI in this role

Senior Data Engineer to design and deliver complex batch and real-time data pipelines using Databricks, Spark, and Python.

databrickssparkpythonpysparkdbtgitlabjenkins

data-engineeringetlci-cddataopstest-driven-development

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-1

Who is Mastercard?
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

Overview
Mastercard is seeking a Senior Data Engineer to join our team in Hyderabad, India. This is an experienced individual-contributor role that owns end-to-end pipeline delivery and mentors peers. It will appeal to you if you combine deep technical expertise with the ability to independently deliver complex data solutions and raise the bar for the engineers around you.

You will bring cutting edge software and full stack development skills with advanced knowledge of cloud and data lake experience while working with massive data volumes. Our teams are small, agile, and focused on the needs of the high growth fintech marketplace, and you will work across functional teams within Mastercard to deliver on our cloud strategy while keeping our systems resilient, responsive, and maintainable on cloud.

Key Responsibilities
Own the end-to-end design, development, and delivery of complex batch and real-time data pipelines using Databricks, Spark, Python, and PySpark.

Drive dbt-based transformation architecture with rigorous Test-Driven Development (TDD) and modular design.

Design and own CI/CD pipelines using GitLab and Jenkins, and champion DataOps practices across the team.

Administer and optimize Databricks objects and the platform using Databricks Asset Bundles, managing performance, access, and cost.

Establish data observability frameworks covering quality, freshness, lineage, and anomaly detection.

Lead the design of real-time data streaming pipelines (e.g., using Kafka, Spark Structured Streaming).

Mentor junior and mid-level engineers, and lead code and design reviews.

Partner with stakeholders on requirements, delivery planning, and technical trade-offs.

Plan and execute deployments, migrations, and upgrades with minimal disruption to operations.

Required Qualifications
Bachelor's degree in computer science or a related technical field (Master's a plus).

5+ years of data engineering experience.

Deep hands-on experience with Databricks (including administration), Spark, and Python.

Strong background in PySpark and distributed data processing.

Proven track record using dbt for robust, testable transformation workflows following TDD.

Familiarity with Databricks Asset Bundles for object deployment and version control.

Strong CI/CD (GitLab, Jenkins) and DataOps expertise.

Experience with real-time data processing and streaming pipelines.

Expert SQL development and strong cloud infrastructure experience (AWS, Azure, or GCP).

Demonstrated experience mentoring engineers and leading technical delivery.

Ideally you have experience in banking, e-commerce, credit cards or payment processing and exposure to both SaaS and premises-based architectures. In addition, you have a post-secondary degree in computer science, mathematics, or quantitative science.

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-1 at Mastercard rates 20 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● 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. Open the description and count.

Prepare for this job

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Skills and AI tools this role asks for

Data EngineeringETLCi CdDataopsTest Driven DevelopmentDatabricksSparkPython

Questions you could be asked

Tell me about a project where data engineering 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 ci cd was part of your work. What did you do?
Tell me about a project where dataops was part of your work. What did you do?
Tell me about a project where test driven development was part of your work. What did you do?

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List these exact terms on your resume: Data Engineering, ETL, Ci Cd, Dataops, and Test Driven Development. 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.

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