Data Engineer: Build Scalable Pipelines & Lakehouse

Bah; Arlington (TX); On-site; USD 62,000 - 141,000; Full time

Job summary

Booz Allen Hamilton is seeking a Data Engineer to design, build, and maintain scalable data pipelines for mission-driven projects. You will work with a multidisciplinary team to organize data from diverse sources and enable analytics for fraud detection, cancer research, and national intelligence.

You will contribute to data pipelines, lakehouse designs, streaming and batch processing, and data quality and observability practices in a fast-paced, agile environment.

Qualifications

• 5+ years of experience designing, building, and maintaining data pipelines in production environments.
• 3+ years of experience developing with distributed systems such as Spark or PySpark.
• 3+ years of experience with data lakehouse or warehouse platforms, schema design, and query optimization.
• 3+ years of experience processing data using streaming, including Kafka or Kinesis, and batch methods.
• Experience in Python and SQL.
• Experience implementing best practices for data quality, testing, and observability.
• Bachelor's degree.

Responsibilities

• Design, build, and maintain scalable data pipelines and platforms for clients.
• Collaborate with analysts, developers, and data consumers in an agile environment.
• Develop and deploy pipelines that organize disparate data for meaningful insights.

Skills

Data pipelines

Spark/PySpark

Python

SQL

Data quality

Observability

Public Trust eligibility

Education

Bachelor's degree

Tools

Spark

Kafka

Kinesis

Docker

Kubernetes

AWS

Databricks

Nifi

Job description

Booz Allen Hamilton is seeking a Data Engineer to design, build, and maintain scalable data pipelines for mission-driven projects. You will work with a multidisciplinary team to organize data from diverse sources and enable analytics for fraud detection, cancer research, and national intelligence.

You will contribute to data pipelines, lakehouse designs, streaming and batch processing, and data quality and observability practices in a fast-paced, agile environment.