Lead Data Engineer

2 weeks ago


Rockville, United States Optomi Full time

Lead Data Engineer | Remote

Optomi, in partnership with a global leader in the financial services industry, is seeking a Lead Data Engineer to join their team This candidate will be responsible for designing, ingesting, storing, validating and disseminating data in a consumable format, for business intelligence teams and data analysts to get deeper business insight from the data.


What the right candidate will have:


  • 7+ years of strong experience in Java or Scala
  • 7+ years of experience in ETL projects
  • 5+ years of experience in big data projects
  • 3+ years of experience with API development (REST API’s)
  • Strong experience in big data technologies like AWS EMR, AWS EKS, Apache Spark
  • Strong experience with technologies like AWS Dynamo DB, AWS Lambda
  • Strong experience in processing with JSON and CSV files
  • Must be able to write complex SQL queries
  • Experience in performance tuning and optimization
  • Familiar with columnar storage formats (ORC, Parquet) and various compression techniques
  • Experience in writing Unix shell scripts
  • Unit testing using JUnit or ScalaTest
  • Git/Maven/Gradle
  • Code Reviews
  • Experience with CI/CD pipelines
  • Believes in Scrum/Agile, and has deep experience delivering software when working on teams that use Scrum/Agile methodology
  • Strong and creative analytical and problem-solving skills


Responsibilities of the right candidate:


  • Understand complex business requirements
  • Design and develop ETL pipeline for collecting, validating and transforming data according to the specification
  • Develop automated unit tests, functional tests and performance tests.
  • Maintain optimal data pipeline architecture
  • Design ETL jobs for optimal execution in AWS cloud environment
  • Reduce processing time and cost of ETL workloads
  • Lead peer reviews and design/code review meetings
  • Provide support for production support operations team
  • Implement data quality checks.
  • Identify areas where machine learning can be used to identify data anomalies



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