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Senior Data Engineer

2 months ago

bangalore, India Visa Full time

Job Description

Visa Consulting & Analytics (VCA) team is a key part of the Global Solutions organization, a high-performing team of data engineer, scientists, data analysts and statisticians helping major organizations adapt and evolve to meet the changes taking place in technology, finance, and commerce, with cutting-edge, creative and advanced analytic solutions.


Visa is looking for Data Engineer Senior Consultant, who will be the responsible for leading the Data Engineering engagements with our partners and supporting end-to-end delivery.

The individual will be accountable for supporting and driving the design, development and implementation of analytics-driven strategies as well as high-impact solutions for Visa clients.


Responsibilities:

  • Strong technology and leadership background building enterprise scale applications using Python/ Pandas /Scala, RDBMS. Machine Learning, Data Engineering (Hadoop, Hive, Spark), NoSQL, REST APIs, Kafka, and Data Pipelines desirable.
  • Design and deploy data and pipeline management frameworks built on top of open-source components, including Hadoop, Hive, Spark, HBase, Kafka streaming and other Big Data technologies.
  • Lead code reviews, ensuring adherence to coding standards, and promoting clean, efficient code within the team.
  • Experience with Continuous Integration and Automated Test tools such as Jenkins, Artifactory, Git, Selenium, Chef desirable
  • Familiarity or experience with data mining, data science, machine learning and statistical modeling (e.g. regression modeling, clustering techniques, decision trees, etc.) is preferred
  • Responsible for the design and implementation of an innovative, scalable, and distributed systems that take advantage of technology to allow standardization, security, timeliness and quality of data.
  • Work with product managers in developing a strategy and road map to provide compelling capabilities that helps them succeed in their business goals.
  • Work closely with senior engineers to develop the best technical design and approach for new product development.
  • Resolve complex technical issues, making technical decisions that impact the project and beyond.
  • Project management: prioritization, planning of projects and features, stakeholder management and tracking of external commitments
  • Identify opportunities for further enhancements and refinements to standards and processes.
  • Strong Negotiation Skills: You will be a distinguished ambassador for product development, collaborating, negotiating, managing tradeoffs and evaluating opportunistic new ideas with business partners
  • Mentor junior data engineers, fostering a culture of continuous learning and innovation.
  • Consult with stakeholders to understand their business objectives and translate them into data-driven solutions. Lead data strategy sessions.
  • Stay updated with the latest industry trends and technologies in data engineering and adopt them as needed.
  • Drive the adoption of data-driven decision making within the organization by promoting the benefits of data engineering solutions.
  • Resolve complex technical issues, making technical decisions that impact the project and beyond.
  • Mentor junior team members, develop departmental procedures and best practices standards.
  • Hire and retain world class talents to deliver data platform projects.


Qualifications

Basic Qualifications

• 10+ yrs. work experience with a bachelor’s degree or 8+ years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) in data engineering and analytics field such as computer science, statistics, finance, economics, or relevant area.


Preferred Qualifications

• Working knowledge of Hadoop ecosystem and associated technologies, (For e.g. Apache Spark, Python, Pandas etc.)

• 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.

• 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: Python , Scala/java, Golang.

• 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 (other cloud) 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 MLOPS 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.