Manager, Data Engineering

Company Description

Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.

Make an impact with a purpose-driven industry leader. Join us today and experience #LifeatVisa.

Job Description

Visa’s Global Sales and Commercial Operations team supports the execution and delivery of key global initiatives and planning activities across the company and reports directly to the CEO.  Our mandate is to develop, design, and execute the strategic direction for Visa’s business operations and global sales organization, be the super connector for the markets, regions, global groups, and bring a consistent voice and ‘One Visa’ mindset.  The team has the following key focus areas: Business Performance, Sales Excellence, Sales Enablement and Sales Tools.    

This role will report into the Business Performance organization which includes Scorecard, Sales compensation strategies and programs (MIP and SIP), Target setting, the TRUTH business performance dashboards, and Client Insights program (survey, touchpoints). The Data Engineering Manager on the team is a specialist who makes data available from new sources, builds robust data models, creates, and optimizes data enrichment pipelines, and provides engineering support to specific projects.  You will partner with our Data Visualizers to ensure data needed by the business is available and accurate and to develop certified data sets.  This is a technical role that acts as a force multiplier to our Visualizers, Analysts, and other data users across the team.

Responsibilities

• Establish data processes and automations, based upon business and technology requirements, leveraging Visa’s supported data platforms and tools

• Deliver small to large data engineering projects either individually or as part of a project team

• Develop and maintain data models and schema designs for efficient data storage and retrieval, with a strong understanding of best practices in data modeling and data architecture

• Design and implement data pipelines to extract, transform, and load data from various sources into the data warehouse, with a strong focus on reusability, performance, scalability and cost efficiency

• Collaborate with cross-functional teams to understand data requirements and ensure data quality, with a focus on implementing data validation and data quality checks at various stages of the pipeline

• Provide expertise in data warehousing, ETL, and data modeling to support data-driven decision making, with a strong understanding of best practices in data pipeline design and performance optimization

• Extract and manipulate large datasets using standard tools such as Hadoop (Hive), SQL, Spark, Python (pandas, NumPy) and Presto

• Provide ongoing production support for monthly Scorecard publishing and TRUTH dashboard updates

• Communicate complex concepts in a clear and effective manner

• Stay up-to-date with the latest data engineering trends and technologies to ensure the company's data infrastructure is always state-of-the-art and following best practices

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.

Qualifications

Basic Qualifications:

• 5 or more years of relevant work experience with a Bachelors Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD

Preferred Qualifications:

• 6+ years of analytics experience with a focus on data engineering
• Strong experience with SQL, Python, and relational databases
• Advanced knowledge of SQL (e.g., understands subqueries, self-joining tables, stored procedures, can read an execution plan, sql tuning, etc.)
• Solid understanding of best practices in data warehousing, ETL, data modeling, and data architecture.
• Strong understanding of best practices in data governance, data validation and data quality checks
• Experience in Python, Spark, and exposure to scheduling tools like Tuber/Airflow is preferred.
• Able to create data dictiona