Data Engineer III

eSimplicity•Columbia, MD

8d•Remote

About The Position

eSimplicity is a modern digital services company that partners with government agencies to improve the lives and protect the well-being of all Americans, from veterans and service members to children, families, and seniors. Our engineers, designers, and strategists cut through complexity to create intuitive products and services that equip federal agencies with solutions to courageously transform today for a better tomorrow. Responsible for developing, expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. Support software developers, database architects, data analysts and data scientists on data initiatives and ensure optimal data delivery architecture is consistent throughout ongoing projects. Creates new pipeline and maintains existing pipeline, updates Extract, Transform, Load (ETL) process, creates new ETL feature , builds PoCs with Redshift Spectrum, Databricks, AWS EMR, SageMaker, etc.; Implements, with support of project data specialists, large dataset engineering: data augmentation, data quality analysis, data analytics (anomalies and trends), data profiling, data algorithms, and (measure/develop) data maturity models and develop data strategy recommendations. Operate large-scale data processing pipelines and resolve business and technical issues pertaining to the processing and data quality. Assemble large, complex sets of data that meet non-functional and functional business requirements Identify, design, and implement internal process improvements including re-designing data infrastructure for greater scalability, optimizing data delivery, and automating manual processes Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS and SQL technologies Building analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition? Working with stakeholders including data, design, product and government stakeholders and assisting them with data-related technical issues Write unit and integration tests for all data processing code. Work with DevOps engineers on CI, CD, and IaC. Read specs and translate them into code and design documents. Perform code reviews and develop processes for improving code quality. Perform other duties as assigned.

Requirements

All candidates must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non-expired passport from their country of birth and appropriate VISA/work permit documentation.
Minimum of 8 years of experience as a Data Engineer or in hands-on software development, including at least 4 years using Python, Java, and cloud technologies for building and maintaining data pipelines.
Bachelor’s degree in Computer Science, Information Systems, Engineering, Business, or a related scientific or technical discipline required.
OR In lieu of a degree, candidates may qualify with 10 years of general information technology experience, including at least 8 years of specialized experience.
Expert data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up.
Self-sufficient and comfortable supporting the data needs of multiple teams, systems, and products.
Experienced in designing data architecture for shared services, scalability, and performance
Experienced in designing data services including API, meta data, and data catalogue.
Experienced in data governance process to ingest (batch, stream), curate, and share data with upstream and downstream data users.
Ability to build and optimize data sets, ‘big data’ data pipelines and architectures?
Ability to perform root cause analysis on external and internal processes and data to identify opportunities for improvement and answer questions?
Excellent analytic skills associated with working on unstructured datasets?
Ability to build processes that support data transformation, workload management, data structures, dependency and metadata
Demonstrated understanding and experience using software and tools including big data tools like Spark and Hadoop; relational databases including MySQL and Postgres; workflow management and pipeline tools such as Apache Airflow, and AWS Step Function; AWS cloud services including Redshift, RDS, EMR and EC2; stream-processing systems like Spark-Streaming and Storm; and object function/object-oriented scripting languages including Scala, Java and Python.?
Flexible and willing to accept a change in priorities as necessary.
Ability to work in a fast-paced, team-oriented environment
Experience with Agile methodology, using test-driven development.
Experience with GitHub and Atlassian Jira/Confluence.
Excellent command of written and spoken English.

Nice To Haves

Federal Government contracting work experience.
Databricks Certification, Google’s Certified Professional-Data-Engineer certification, IBM Certified Data Engineer – Big Data certification, CCP Data Engineer for Cloudera
Centers for Medicare and Medicaid Services (CMS) or Health Care Industry experience
Experience with healthcare quality data including Medicaid and CHIP provider data, beneficiary data, claims data, and quality measure data.

Responsibilities

Developing, expanding and optimizing our data and data pipeline architecture
Optimizing data flow and collection for cross functional teams
Supporting software developers, database architects, data analysts and data scientists on data initiatives
Ensuring optimal data delivery architecture is consistent throughout ongoing projects
Creating new pipeline and maintains existing pipeline
Updating Extract, Transform, Load (ETL) process
Creating new ETL feature
Building PoCs with Redshift Spectrum, Databricks, AWS EMR, SageMaker, etc.
Implementing large dataset engineering: data augmentation, data quality analysis, data analytics (anomalies and trends), data profiling, data algorithms, and (measure/develop) data maturity models and develop data strategy recommendations
Operating large-scale data processing pipelines and resolve business and technical issues pertaining to the processing and data quality
Assembling large, complex sets of data that meet non-functional and functional business requirements
Identifying, designing, and implementing internal process improvements including re-designing data infrastructure for greater scalability, optimizing data delivery, and automating manual processes
Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS and SQL technologies
Building analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition
Working with stakeholders including data, design, product and government stakeholders and assisting them with data-related technical issues
Writing unit and integration tests for all data processing code
Working with DevOps engineers on CI, CD, and IaC
Reading specs and translate them into code and design documents
Performing code reviews and develop processes for improving code quality
Performing other duties as assigned