MastercardData Engineer II
Mastercard is hiring a Data Engineer II in Pune, India. Level rates it ; you can apply on Level.
AI in this role
Build and maintain large-scale data platforms, ETL processes, and database environments supporting data science and analytics.
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
Data Engineer IIOverviewWe are looking for a Data Engineer to join the Data Engineer team. This is a pivotal role, responsible for supporting multiple Data Analysts and Data Scientists day-to-day and assisting various data-driven requests across the company. The position involves a wide variety of technical skills, from coding to infrastructure, from data analysis to database management. The ideal candidate will have experience working with large-scale data and building automated data ETL processes for large-scale data analysis. ODS or data warehousing experience would be ideal.
Key responsibilities include:
• Build and maintain database technologies, environments, and applications, seeking opportunities for improvements and efficiencies
• Should be familiar and have experience in cloud data platforms like Databricks and/or Cloudera, Iceberg/Delta tables
• Writes SQL statement procedures to optimize SQL execution and query development
• Effectively utilizes various technology like Spark (Scala, Python), Apache Kafka, Trino
• Manages the deployment of data solutions that are optimally standardized and database updates to meet project deliverables
• Leads database security posture, which includes proactively identifying security risks and implementing both risk mitigation plans and control functions
• Supports process improvement efforts to identify and test opportunities for automation and/or reduction in time to deployment
• Participating in the entire solution development cycle from requirement gathering to solution deployment.
• Working with multiple stakeholders across teams to fulfill ad-hoc investigations, including large-scale data extraction, transformation, and analyses.
• Supporting production code to produce comprehensive and accurate data artifacts.
• Promoting strategies to improve our data modeling, quality, and architecture.
All About You:
Should have experience working with Databricks and/or Cloudera platforms
Skilled in Spark framework (Scala/Python), Apache Kafka, Spark Streaming
Highly skilled in SQL knowledge and relational databases.
Experienced with developing and maintaining data pipelines for the extraction, transformation, and/or loading of large-scale data.
Experienced with working with large data sets. You’ve implemented parallel processing and have dealt with the limitations and challenges of working with terabytes of data.
Strong analytics skills related to working with unstructured datasets.
Experienced with performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Experienced with database administration and query optimization with different databases.
Experienced with at least one of the programming languages like Python
Capable of effectively communicating requirements and solutions to audiences with different skill sets.
Familiarity with the core requirements of data analysis and/or presentation.
Focused, innovative, and self-sufficient problem solver who is willing to independently and creatively deep-dive into an issue, cut to the heart of a problem, and solve it.
Bonus Points if:
You have any prior experience with any of the following:
• AWS; S3, EMR, Ozone, Iceberg
• Python, Javascript, PLSQL, regex
• Jenkins, Datadog, Splunk, Dynatrace
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
Data Engineer II at Mastercard rates 30 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.
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
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
- $150k
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
- 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 data warehousing was part of your work. What did you do?
- Tell me about a project where database management was part of your work. What did you do?
- Walk me through how you've used Databricks in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Data Engineering, ETL, Data Warehousing, Database Management, 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.
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