▷ [15h Left] Staff Data Engineer

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 Life at Visa 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 Team Summary The Risk and Identity Solutions RaIS team provides risk management services for banks merchants and other payment networks Machine learning and AI models are the heart of the real-time insights used by our clients to manage risk Created by the Visa Predictive Models VPM team continual improvement and efficient deployment of these models is essential for our future success To support our rapidly growing suite of predictive models we are looking for engineers who are passionate about managing large volumes of data creating efficient automated processes and standardizing ML AI tools This is a great opportunity to work with a new Data Engineering and MLOps team to scale and structure large scale data engineering and ML AI that drives significant revenue for Visa As a member of the Risk and Identify Solutions modeling organization VPM your role will involve developing and implementing practices that will allow deployment of machine learning models in large data science projects You must be a hands-on expert able to navigate both data engineering and data science disciplines to build effective engineering solutions that support ML AI models You will partner closely with global stakeholders in RaIS Product VPM Data Science and Visa Research to help create and prioritize our strategic roadmap You will then leverage your expert technical knowledge of data engineering tools and data architecture in the design and creation of the solutions on our roadmap The position is based at Visa s offices in Bangalore India This is a hybrid position Expectation of days in office will be confirmed by your hiring manager Qualifications Qualifications 7 yrs work experience with a bachelor s degree or 6 years of work experience with a Master s or Advanced Degree in an analytical field such as computer science statistics finance economics or relevant area Working knowledge of Hadoop ecosystem and associated technologies For e g Apache Spark Python Pandas etc Technical skills Strong experience in creating large scale data engineering pipelines data-based decision-making and quantitative analysis Experience with SQL for extracting aggregating and processing big data Pipelines using Hadoop EMR NoSQL Databases Experience with complex high volume multi-dimensional data as well as machine learning models based on unstructured structured and streaming datasets Preferred skills ETL processes The role also involves developing and executing large scale ETL processes to support data quality reporting data marts and predictive modeling Spark pipelines The role requires building and maintaining efficient and robust Spark pipelines to create and access data sets and feature stores for ML models Experience in writing and optimizing spark code and Hive code to process Large Data Sets in Big-Data Environments Strong Development experience in more than one of the following Golang Java Python Rust Knowledge of standard big data and Real Time stack such as Hadoop Spark Kafka Redis Flink and similar technologies Hands on experience in building and maintaining data pipelines feature engineering pipelines and comfortable with core ML concepts Hands on experience in engineering testing validating and productizing AL ML models for high performance use cases Exposure to model serving engines such as TensorFlow Triton etc Exposure to model development frameworks like Ml flow Proficient in managing and operating AWS services including EC2 S3 SageMaker etc Proficient in setting up and managing distributed data and computing environments using AWS services Knowledge about DR HA topologies Reliability Engineering with hands on experience in implementing the same Knowledge of using and maintaining DevOps tools and implementing automations for production Experience of working with containerized and virtualized environments Docker K8s Experience with Unix Shell or Python scripting and exposure to Scheduling tools like Airflow and Control - M Experience creating supporting production software systems and a proven track record of identifying and resolving performance bottlenecks for production systems Exposure to deploying large scale ML AI models built by the data science teams and experience with development of models is a strong plus Exposure to public cloud equivalents and ecosystem shall be a plus Strong Experience with Visualization Tools like Tableau Power BI is a plus Additional Information Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race color religion sex national origin sexual orientation gender identity disability or protected veteran status Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law