Lead Data Engineer, Data Engineering
Mastercard
Lead Data Engineer, Data Engineering
Job Description Summary
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.
We are seeking a Lead Data Engineer, Data Engineering who will: • Perform data ingestion, aggregation, processing to drive and enable relevant insights from available data sets.
. Leveraging platform like Cloudera Data Engineering (CDE), Cloudera AI (CAI), Databricks, AWS to deliver the product engineering solutions.
• Partner with various teams (i.e., Product Manager, Data Science, Platform Strategy, Technology) on data needs/requirements in order to deliver data solutions that generate business value.
• Manipulate and analyze complex, high-volume, high-dimensionality data from varying sources using a variety of tools and data analysis techniques.
• Identify innovative ideas and deliver proof of concepts, prototypes to deliver against the existing and future needs and propose new products, services and enhancements.
• Integrate new data assets which increase the value proposition for our customers and enhance our existing solutions and services.
• Analyse large volumes of transaction and product data to generate insights and actionable recommendations to drive business growth
• Collect and synthesize feedback from clients, development, product and sales teams for new solutions or product enhancements.
• Apply knowledge of metrics, measurements, and benchmarking to complex and demanding solutions. All about You
• Good understanding of Python – Pandas, Numpy, PySpark and Impala.
• Experience in doing data analysis, development, testing on Hadoop and Databricks Cloud platform.
• Strong SQL and higher-level programming languages with solid knowledge of data mining, machine learning algorithms and tools
• Experience (Good to have) with data integration tools – ETL/ELT tools (i.e. Apache NiFi, Azure Data Factory, Pentaho, Talend)
• Experience with Graph Database is a plus.
• Experience in hands-on data modeling, programming, querying, data mining and report development using large volumes of granular data to deliver business intelligence and custom reporting solutions.
• Exposure to collecting and/or working with data including standardizing, summarizing, offering initial observations and highlighting inconsistencies.
• Strong understanding of the application of analytical methods and data visualization to support business decisions.
• Ability to understand complex operational systems and analytics/business intelligence tools for the delivery of information products and analytical offerings to a large, global user base.
• Able to work in a fast-paced, deadline-driven environment as part of a team and as an individual contributor
• Ability to easily move between business, analytical, and technical teams and articulate solution requirements for each group 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.
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