Data Engineer
Core
Design, build, and maintain data infrastructure to ensure seamless data flow, scalability, and reliability for the US federal government.
Role type
Data Engineer
Builds
Data pipelines, data infrastructure, and production ML model integrations
Domain
US Federal Government / Defense & National Security
Deliverable
production ML models | data pipelines | infrastructure
Required skills
Python, SQL, data pipeline orchestration (Apache Airflow, dbt, Prefect, Dagster), cloud platforms (AWS preferred), big data processing (Apache Spark, Databricks, Apache Kafka), MLOps tools (Amazon SageMaker, MLflow, Kubeflow), monitoring and troubleshooting
Preferred skills
large-scale distributed systems, data governance and security best practices, cross-functional collaboration
Technologies
Python, Apache Airflow, dbt, Prefect, Dagster, AWS, GCP, Azure, SQL, PostgreSQL, Snowflake, Amazon Redshift, BigQuery, Apache Spark, Databricks, Apache Kafka, Amazon SageMaker, MLflow, Kubeflow, Amazon CloudWatch, Datadog, Great Expectations, Git, Jira, Confluence (via careerplan.io/jobs/4680730006-data-engineer-at-accenturefederalservices)
Responsibilities
Write clean, efficient, and scalable code to build and optimize data solutions; Design, build, and orchestrate robust and reliable data workflows; Work comfortably in cloud environments; Extract, integrate, and ensure the quality of data from various sources; Leverage frameworks to process and manage large-scale data workflows; Support the implementation, deployment, and scaling of machine learning models in production environments; Monitor data pipeline health, troubleshoot issues, and ensure data consistency; Work closely with data scientists, analysts, and other stakeholders to understand data requirements, communicate solutions, and document processes
Seniority
Mid-level (2+ years experience)
