Lead, Data Engineer

Overview

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$/yr - $/yr

Job Classification:
Technology - Engineering & Cloud

Are you interested in building capabilities that enable the organization with innovation, speed, agility, scalability and efficiency? The Global Technology team takes great pride in our culture where digital transformation is built into our DNA! When you join our organization at Prudential, you’ll unlock an exciting and impactful career – all while growing your skills and advancing your profession at one of the world’s leading financial services institutions.

As a Lead Data Engineer, you will partner with talented architects, infrastructure engineers, machine learning engineers, data scientists and data analysts to improve major products and services. You will analyze, design, develop, test, and perform ongoing maintenance to build high quality data pipelines that drive platform delivery. You will implement capabilities to solve sophisticated business problems, deploy innovative products, services and experiences to delight our customers.

In addition to advanced technical expertise and experience, you will bring excellent problem solving, communication and teamwork skills, along with agile ways of working, strong business insight, an inclusive leadership attitude and a continuous learning focus to all that you do.

• Build and optimize data pipelines, logic and storage systems with latest coding practices and industry standards and modern design patterns and architectural principles; remove technical impediments
• Develop high quality, well documented and efficient code adhering to all applicable Prudential standards
• Conduct complex data analysis and report on results, prepare data for prescriptive and predictive modeling, combine raw information from different sources
• Collaborate with data analysts, scientists, and architects on data projects to enhance data acquisition, transformation, organization processes, data reliability, efficiency, and quality
• Write unit, integration tests and functional automation, researching problems discovered by quality assurance or product support, developing solutions to address the problems
• Bring a strong understanding of relevant and emerging technologies, provide input and coach team members and embed learning and innovation in the day-to-day
• Work on complex problems in which analysis of situations or data requires an evaluation of intangible variables
• Use programming languages including but not limited to Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting

• Bachelor of Computer Science or Engineering or experience in related fields
• Experience in working with Dev Ops automation tools & practices;
  Knowledge of full software development life cycle (SDLC)
• Ability to coach others with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization
• Knowledge of business concepts tools and processes that are needed for making sound decisions in the context of the company s business
• Ability to learn new skills and knowledge on an on-going basis through self-initiative and tackling challenges
• Excellent problem solving, communication and collaboration skills; enjoy learning new skills!
• Advanced experience and/or expertise with several of the following:
• Programming Language:
  Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting
• Data Ingestion, Integration & Transformation:
  Moving data from multiple sources, formats, and volumes to analytics platforms through various tools. Preparing data for further analysis; transforming and mapping raw data to generate insights and wrangling data through tools.
• Database Management System:
  Storing, organizing, managing, and delivering data using relational DBs, No
  
  SQL DBs, Graph DBs, and data warehouse technologies including AWS Redshift and Snowflake
• Database tools:
  Data architecture to store, organize, and manage data.
  
  Experience with SQL and No
  
  SQL based databases for storage and processing of structured, semi-structured & unstructured data.
• Real-Time Analytics:
  Spark, Kinesis Data Streams
• Data Buffering:
  Kinesis, Kafka
• Workflow Orchestration:
  Airflow, App Flow, Austosys, Cloudwatch, Splunk
• Data Visualization:
  Tableau, Power BI, MS Excel
• Data Lakes & Warehousing:
  Building Data Models, Data Lakes and Data Warehousing
• Data Protection and Security:
  Knowledge of data protection, security principles and services; data loss prevention, role based access controls, data encryption, data access capture and core security services
• Common Infrastructure as Code (IaC) Frameworks:
  Ansible, Cloud Formation
• Cloud Computing:
  Knowledge of fundamentals of AWS architectural principles and services;
  Strong ability on cloud formation and to write code;
  Knowledge of AWS core services
• Testing/Quality:
  Unit, interface and end user testing concepts and tooling inclusive of…