The Data Engineer is responsible for building Data Engineering Solutions using next generation data techniques. The individual will be working directly with product owners, customers and technologists to deliver data products/solutions in a collaborative and agile environment.
Responsibilities:
Responsible for design and development of big data solutions. Partner with domain experts, product managers, analyst, and data scientists to develop Big Data pipelines in Hadoop
Responsible for moving all legacy workloads to cloud platform
Work with data scientist to build Client pipelines using heterogeneous sources and provide engineering services for data science applications
Ensure automation through CI/CD across platforms both in cloud and on-premises
Define needs around maintainability, testability, performance, security, quality and usability for data platform
Drive implementation, consistent patterns, reusable components, and coding standards for data engineering processes
Convert SAS based pipelines into languages like PySpark, Scala to execute on Hadoop and non-Hadoop ecosystems
Tune Big data applications on Hadoop and non-Hadoop platforms for optimal performance
Evaluate new IT developments and evolving business requirements and recommend appropriate systems alternatives and/or enhancements to current systems by analyzing business processes, systems and industry standards.
Applies in-depth understanding of how data analytics collectively integrate within the sub-function as well as coordinates and contributes to the objectives of the entire function.
Produces detailed analysis of issues where the best course of action is not evident from the information available, but actions must be recommended/taken.
Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.
Qualifications:
8+ years of total IT experience
5+ years of experience with Hadoop (Cloudera)/big data technologies
Advanced knowledge of the Hadoop ecosystem and Big Data technologies Hands-on experience with the Hadoop eco-system (HDFS, MapReduce, Hive, Pig, Impala, Spark, Kafka, Kudu, Solr)
Experience on designing and developing Data Pipelines for Data Ingestion or Transformation using Java or Scala or Python.
Experience with Spark programming (pyspark or scala or java)
Expert level building pipelines using Apache Spark Familiarity with core provider services from AWS, Azure or GCP, preferably having supported deployments on one or more of these platforms
Hands-on experience with Python/Pyspark/Scala and basic libraries for machine learning is required;
Exposure to containerization and related technologies (e.g. Docker, Kubernetes)
Exposure to aspects of DevOps (source control, continuous integration, deployments, etc.)
Proficient in programming in Java or Python with prior Apache Beam/Spark experience a plus.
System level understanding - Data structures, algorithms, distributed storage & compute
Can-do attitude on solving complex business problems, good interpersonal and teamwork skills
Possess team management experience and have led a team of data engineers and analysts.
Experience in Snowflake is a plus.
Education:
Bachelor’s degree/University degree or equivalent experience
