Senior Data Engineer( DWH)

Title and Summary

Senior Data Engineer

Job Description Summary

Senior Data Engineer (DW)

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 looking for a Senior Software Engineer to join the Account Level Management(ALM) team in our Pune office that’s focused on building ALM Services with Data warehouse skills. The Mastercard Account Level Management platform empowers real-time card level decisioning. As consumers progress along their life stages as card holders, with increasing disposable income and more refined preferences, ALM provides services to issuers so they can effectively offer more relevant benefits and rewards at each stage, to drive loyalty and spend.

• Role

o Develop high quality, secure and scalable data pipelines using spark, Scala/Python/Java on Hadoop or object storage like MinIO.

o Leverage technologies and solutions to innovate with increasingly large data sets.

o Drive automation and efficiency in Data ingestion, data movement and data access workflows by innovation and collaboration.

o Understand, implement and enforce Software development standards and engineering principles in the Big Data space.

o Contribute ideas to help ensure that required standards and processes are in place and actively look for opportunities to enhance standards and improve process efficiency.

o Perform assigned tasks and production incident independently.

• Skill Requirements:

• MUST Technical

• 5-9 years of experience in Data Warehouse/Data Lake/Lake House related projects in product or service-based organization

• Expertise in Data Engineering and implementing multiple end-to-end DW projects in Big Data environment handling petabyte scale data.

• Solid Experience of building complex data pipelines through Spark with Scala/Python/Java on Hadoop or Object storage

• Experience of working with Databases like Oracle, Netezza and have strong SQL knowledge.

• Proficient in working within an Agile/Scrum framework, including creating user stories with well-defined acceptance criteria, participating in sprint planning and reviews

• Write and maintain Unix shell scripts, Oracle SQL, PL/SQL, and perform SQL tuning.

• Optimize and troubleshoot Spark applications for performance, scalability, and fault tolerance

• Use Git-based version control systems and CI/CD pipelines (e.g., Jenkins).

• Implement and manage HIVE external tables, partitions, and various file formats.

• Work across on-premises and cloud environments (AWS, Azure, Databricks).

• Strong experience with Hadoop ecosystem and Cloudera Data Platform (CDP).

• Optional Technical

• Experience of building Nifi pipelines (Preferred)

• Strong analytical skills required for debugging production issues, providing root cause and implementing mitigation plan

• Strong communication skills - both verbal and written

• Ability to multi-task across multiple projects, interface with external / internal resources

• Proactive, detail-oriented and able to function under pressure in an independent environment along with a high degree of initiative and self-motivation to drive results

• Willingness to quickly learn and implement new technologies, participate POC to explore best solution for the problem statement

• Experience working diverse and geographically distributed project teams

• Education:

• Bachelor’s degree in information systems, Information Technology, Computer Science or Engineering or equivalent work experience.