The company is looking to hire a Data Engineer to support federal clients in designing and implementing scalable data pipelines, cloud-native architectures, and modern data platforms to drive mission impact.
Requirements
- Strong experience with Python, SQL, and AWS services (EC2, S3, RDS, Redshift, Lambda, Glue, etc.).
- Hands-on experience with Databricks (Notebooks, Spark clusters, Delta Lake, cloud storage integration).
- Proficiency in SQL and NoSQL databases.
- Proven ability to design, build, and deploy ETL/ELT pipelines.
- Experience with big data tools (Spark, Hadoop, Kafka) and Spark performance tuning in Databricks environments.
- Ability to work with APIs for data retrieval and system integration.
- Strong understanding of data modeling, schema design, and performance tuning.
Responsibilities
- Design and implement modern, scalable data pipelines (ETL/ELT) using Databricks, Spark, and AWS services.
- Migrate and modernize legacy data platforms to cloud environments (AWS preferred; Azure/GCP acceptable).
- Build ingestion mechanisms for batch and real-time data, integrating APIs and custom connectors.
- Develop robust data models and schema designs for transactional, warehouse, and analytics systems.
- Write efficient, well-documented Python and SQL code to enable scalable data processing.
- Leverage orchestration tools (Airflow, Step Functions, etc.) to automate and optimize workflows.
- Collaborate with BI developers and data scientists to deliver structured, high-quality data for analytics and machine learning.
Other
- 10+ years of experience
- Active Public Trust clearance or ability to obtain one.
- Partner with clients, business leaders, and technical teams to align data architecture with mission and business goals.
- Participate in Agile ceremonies, track deliverables, and support development of technical documentation and briefings.
- Excellent written and verbal communication skills.
