Client Services BI & Analytics – Senior Consultant

Company Description

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across 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.

When you join Visa, you join a culture of purpose and belonging – where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world – helping unlock financial access to enable the future of money movement.

Join Visa: A Network Working for Everyone.

Job Description

Job Summary

The Client Services BI & Analytics team strives to create an open, trusting data culture where the cost of curiosity – the number of steps, amount of time, and complexity of effort needed to use operational data to derive insights – is as low as possible.  We govern Client Services’ operational data and metrics, create easily usable dashboards and data sources, and analyze data to share insights.

We are a part of the Client Services Global Business Operations function and work with all levels of stakeholders, from executive leaders sharing insights with the C-Suite to customer-facing colleagues who rely on our assets to incorporate data to their daily responsibilities.

The Data Engineer 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 and Solution Designers 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 Client Services.

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), Spark, Python (pandas, NumPy), Presto, and SQL
• Develop data solutions using Agile principles
• Provide ongoing production support
• 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, with an understanding of best practices in cloud-based data engineering.

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
• 8+ years of work experience with a Bachelor’s Degree in STEM field.

Preferred Qualifications
• 10+ years of analytics experience with a focus on data engineering
• Experience with both traditional data warehousing tools and techniques (such as SSIS, ODI and on prem SQL Server, Oracle) as well as modern technologies (such as Hadoop, Denodo, Spark, Airflow, and Python), and a solid understanding of best practices in 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 val