Databricks Lakehouse Engineer and Analyst, Mid

Booz Allen Hamilton•McLean, VA
•$77,600 - $176,000•Onsite

About The Position

Our U.S. government agency client is building a modern Databricks Lakehouse environment to support real‑time fraud detection, investigative analytics, and AI‑driven risk insights. We are seeking a hands‑on, versatile Databricks engineer who can contribute across data engineering, functional analysis, governance, and model integration in a federal environment. This role combines responsibilities across data engineering, platform configuration, fraud analytics enablement, and stakeholder coordination. Work with us as we use data science for good.

Requirements

  • 4+ years of experience in data engineering, analytics engineering, or ML engineering
  • Experience with Databricks, Delta Lake, and PySpark
  • Experience with structured streaming and medallion architecture
  • Experience supporting fraud analytics, risk scoring, or anomaly detection
  • Experience working independently in a fast‑moving, high‑impact environment
  • Knowledge of Unity Catalog governance and access controls, and text mining or ML techniques
  • Knowledge of ML, AI, or Natural Language Processing (NLP)
  • Ability to collaborate with fraud operations and technical stakeholders
  • Secret clearance
  • Bachelor’s degree

Nice To Haves

  • 3+ years of experience in the development of algorithms leveraging R, Python, SQL, or NoSQL
  • 3+ years of experience with distributed data or computing tools, including MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL
  • 2+ years of experience with ML, AI, or NLP
  • Experience with visualization packages, including Plotly, Seaborn, or ggplot2
  • Possession of strong communication skills, for both technical and non‑technical audiences
  • Possession of strong problem-solving skills
  • Master’s degree

Responsibilities

  • Contribute across data engineering, functional analysis, governance, and model integration in a federal environment.
  • Combine responsibilities across data engineering, platform configuration, fraud analytics enablement, and stakeholder coordination.

Benefits

  • health, life, disability, financial, and retirement benefits
  • paid leave
  • professional development
  • tuition assistance
  • work-life programs
  • dependent care
  • recognition awards program
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