Data Engineer

Booz Allen Hamilton•Chantilly, VA

31d•$99,000 - $225,000•Remote

About The Position

As a data engineer at Booz Allen, you’ll use your expertise to help build advanced technology solutions and lead data engineering activities on some of the most mission-driven projects in the industry. You’ll guide data engineering activities by overseeing the development and deployment of pipelines and platforms that organize and make disparate data meaningful.

Requirements

7+ years of experience in application development and designing, developing, operationalizing, and maintaining complex enterprise‑scale data applications
5+ years of experience developing software and solutions for retrieving, parsing, and processing structured and unstructured data, including scalable ETL and ELT workflows for reporting and analytics
Experience developing scripts, programs, and automated processes to convert various data types into usable formats and support scalable, monitored, and reliable data platforms
Experience with data architecture, data engineering, and enterprise data migration, including leading modernization initiatives and migration efforts involving multiple applications or delivery teams
Experience with ETL design and optimization using Informatica PowerCenter, including Designer, Workflow Manager, Monitor, mapping, and session development and Informatica Developer for data transformation
Experience with relational databases, such as SQL Server, Oracle, and PostgreSQL, including SQL development, data modeling, heterogeneous migrations, stored procedures, and performance tuning
Experience with Linux fundamentals, AWS cloud data architectures such as S3, RDS, cloud‑based processing, and migration and scalable distributed data platforms
Experience supporting CI/CD and DevOps practices using Git, GitLab, GitHub, pipeline implementation, automated deployments, Ansible, and workflow orchestration, including Control‑M
TS/SCI clearance with a polygraph
Bachelor’s degree in a Computer Science, Information Systems, or Engineering field

Nice To Haves

10+ years of experience using Python, SQL, Scala, or Java
5+ years of experience with a public cloud, including AWS, Microsoft Azure, or Google Cloud
5+ years of experience with Distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka
5+ years of experience working on real-time data and streaming applications
5+ years of experience with NoSQL implementation, including MongoDB or Cassandra
5+ years of experience with data warehousing, including AWS Redshift, MySQL, or Snowflake
5+ years of experience with UNIX and Linux, including basic commands and Shell scripting
5+ years of experience with Agile engineering practices
Experience in executing full data migration lifecycle activities, including unit testing, UAT support, reconciliation, validation, production deployment, and implementing data security controls

Responsibilities

Overseeing the development and deployment of pipelines and platforms that organize and make disparate data meaningful.
Building advanced technology solutions.
Leading data engineering activities on mission-driven projects.
Developing software and solutions for retrieving, parsing, and processing structured and unstructured data.
Developing scalable ETL and ELT workflows for reporting and analytics.
Developing scripts, programs, and automated processes to convert various data types into usable formats.
Supporting scalable, monitored, and reliable data platforms.
Leading modernization initiatives and migration efforts involving multiple applications or delivery teams.
Designing and optimizing ETL using Informatica PowerCenter.
Developing mappings and sessions using Informatica PowerCenter.
Performing data transformation using Informatica Developer.
Developing SQL for relational databases.
Performing data modeling.
Conducting heterogeneous migrations.
Developing stored procedures.
Performing performance tuning on relational databases.
Supporting CI/CD and DevOps practices.
Implementing pipelines.
Performing automated deployments.
Orchestrating workflows.
Executing full data migration lifecycle activities, including unit testing, UAT support, reconciliation, validation, production deployment, and implementing data security controls.