Mastercard is Hiring for Software Engineer 1

Mastercard is hiring for Data Engineer

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Job Details

  1. Company: Mastercard
  2. Job Position: Lead Data Engineer
  3. Job Location: Hyderabad, Telangana, India – 500043
  4. Job Type: Full-time
  5. Experience: 8+ years
  6. Qualification: Bachelor’s or Master’s degree in Computer Science or a related technical field
  7. Salary: Estimated ₹35 LPA–₹60 LPA, depending on experience, technical expertise, and interview performance. This is an indicative estimate, not an officially disclosed salary range.
  8. Job Category: AI and Data Engineering
  9. Job ID: R-292041
  10. Application Deadline: October 30, 2026
  11. Seniority: Lead-level individual contributor with technical leadership responsibilities

Job Overview

Mastercard is hiring an experienced Lead Data Engineer for its Hyderabad office. The role involves designing and managing scalable data platforms using Databricks, Apache Spark, Python, PySpark, and dbt. The selected candidate will establish engineering standards, guide architecture decisions, improve deployment processes, and mentor other engineers. This position combines hands-on data engineering with technical leadership, platform administration, and collaboration with product and business stakeholders. Candidates with extensive experience in cloud-based data platforms and a background in banking, e-commerce, or payment processing may have an advantage.

Key Responsibilities

  1. Establish and maintain engineering standards for data pipelines built with Databricks, Spark, Python, PySpark, and dbt.
  2. Provide technical leadership and guidance to data engineering teams.
  3. Lead architecture discussions and review technical designs.
  4. Design and manage CI/CD pipelines using GitLab and Jenkins.
  5. Establish DataOps practices and data observability strategies.
  6. Define Databricks administration standards and platform governance.
  7. Manage deployments using Databricks Asset Bundles.
  8. Resolve complex technical problems involving data pipelines and platforms.
  9. Mentor engineers and promote code quality through reviews and best practices.
  10. Collaborate with product teams and stakeholders on technical roadmaps.
  11. Evaluate technical trade-offs and recommend suitable solutions.
  12. Lead data platform deployments, migrations, and upgrades.
  13. Improve data platform performance, cost efficiency, and reliability.
  14. Strengthen testing practices, including test-driven development (TDD).
  15. Ensure data engineering activities follow security policies and information protection standards.
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Eligibility Criteria

  1. Bachelor’s or Master’s degree in Computer Science or a related technical field.
  2. At least 8 years of professional data engineering experience.
  3. Demonstrated experience providing technical leadership to engineering teams.
  4. Advanced hands-on knowledge of Databricks, including platform administration.
  5. Strong experience with Apache Spark, Python, and PySpark.
  6. Deep knowledge of dbt, test-driven development, and CI/CD practices.
  7. Experience with GitLab and Jenkins.
  8. Ability to define engineering standards and lead architecture decisions.
  9. Experience managing platform observability, performance, and costs.
  10. Excellent communication, mentoring, and stakeholder management skills.
  11. Experience in banking, e-commerce, credit cards, or payment processing is an advantage.
  12. Familiarity with SaaS and on-premises architectures is beneficial.

Required Skills

  1. Databricks platform engineering and administration.
  2. Apache Spark and PySpark.
  3. Python programming and data pipeline development.
  4. dbt for data transformation and modelling.
  5. CI/CD automation using GitLab and Jenkins.
  6. DataOps and data observability.
  7. Databricks Asset Bundles and deployment governance.
  8. Data architecture and platform design.
  9. Test-driven development and automated testing.
  10. Data platform migration and upgrades.
  11. Performance tuning and cost optimization.
  12. Code reviews and engineering best practices.
  13. Technical mentoring and team leadership.
  14. Data security and governance.
  15. Stakeholder communication and technical roadmap planning.

Why Join Mastercard?

  1. Work with a globally recognized technology company in the digital payments industry.
  2. Lead the technical direction of large-scale data engineering platforms.
  3. Work with modern technologies such as Databricks, Spark, Python, and dbt.
  4. Influence engineering standards and architecture decisions.
  5. Mentor engineers and strengthen team capabilities.
  6. Collaborate with cross-functional teams on cloud and data platform initiatives.
  7. Gain exposure to complex data challenges in the financial technology sector.
  8. Contribute to secure, scalable, and reliable digital payment infrastructure.
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