Data Scientist (Data & AI Engineer)

Responsibilities

1. Design, develop, and maintain scalable data pipelines and systems.
2. Monitor and troubleshoot data pipeline issues to ensure seamless data flow.
3. Establish data processes and automation based on business and technology requirements, leveraging Visa’s supported data platforms and tools
4. Deliver small to large data engineering and Machine learning projects either individually or as part of a project team
5. Setup ML Ops pipelines to Productionalize ML models and setting up Gen AI pipelines
6. 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
7. 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
8. Extract and manipulate large datasets using standard tools such as Hadoop (Hive), Spark, Python (pandas, NumPy), Presto, and SQL
9. Develop data solutions using Agile principles
10. Provide ongoing production support
11. Communicate complex concepts in a clear and effective manner
12. 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 remote position. A remote position does not require job duties be performed within proximity of a Visa office location. Remote positions may be required to be present at a Visa office with scheduled notice.

Qualifications

Primary / Basic Skills

1. 3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
2. 3+ years of work experience with a bachelor’s degree in the STEM field.
3. Strong experience with SQL, Python, Hadoop, Spark, Hive, Airflow and MPP data bases
4. 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)
5. Advanced knowledge of SQL (e.g., understands subqueries, self-joining tables, stored procedures, can read an execution plan, SQL tuning, etc.)
6. Solid understanding of best practices in data warehousing, ETL, data modeling, and data architecture.
7. A team player and collaborator, able to work well with a diverse group of individuals in a matrixed environment

Preferred Skills

1. A solid understanding of best practices in data engineering
2. Strong analytics experience with a focus on Data Engineering and AI
3. Experience with cloud-based data warehousing and data pipeline management (AWS, GCP, Azure)
4. Experience with NoSQL databases (e.g., MongoDB, Cassandra)
5. Maintainance and Support of Data pipelines (daily / monthly)
6. Experience with visualization software (e.g., Tableau, QlikView, PowerBI) is a plus.