M9 Solutions
IT Services
DataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Engineer at M9 Solutions. Skills: Data Engineering, Python, Cloud Services, Data Pipelines. Collect and integrate data. Design and implement ETL/ELT pipelines”
Industry & Context.
Active TS/SCI clearance, Work onsite in Springfield, VA
What They're Looking For.
Must Have
Active TS/SCI security clearance, Bachelor's or master’s degree in computer science, engineering, or related field, 10+ years of experience in data engineering or software development roles, Proficiency in Python, Solid experience with cloud services (AWS or Azure), Proven experience in building and maintaining data pipelines using Kafka, Airflow, Grasp of database internals
Nice to Have
Knowledge of data cataloging tools, Knowledge of semantic layer design, Experience with containerization (Docker), Experience with orchestration (Kubernetes), Familiarity with MLOps tools or platforms
What You'll Do.
Collect and integrate data
Design and implement ETL/ELT pipelines
Build and deploy data pipelines
Understand and optimize storage engines
Handle high-volume data streams
Schedule and monitor data workflows
Integrate LLMs and ML models
How You'll Work.
Team & Collaboration
Collaborate with data scientists
Full Job Description
M9 Solutions is dedicated to providing IT services and solutions to the Federal Government by mobilizing the right people, skills, clearance levels, and technologies to help organizations that desire improved performance and modern, sustainable change. M9 has provided quality IT services and support to more than 30 Federal Agencies and multiple commercial customers nationwide. Our capabilities include IT Talent Solutions, Data Delivery & Analytics, Cyber Security, Cloud Migration, Applications and Infrastructure, Software Development, and Finance & Accounting. M9 Solutions is seeking a Data Engineer to work onsite in support of a government contract for a client located in Springfield, VA. An active TS/SCI clearance is required. Responsibilities Data Ingestion & Acquisition: Collect and integrate data from a wide variety of structured and unstructured sources, including APIs, RDBMS, file systems, third-party services, and real-time streams. Pipeline Development: Design and implement scalable ETL/ELT pipelines to clean, enrich, normalize, and semantically align data (ontology-driven transformations). Cloud Deployment: Build and deploy data pipelines and associated infrastructure on AWS or Azure, using managed services like Lambda, Glue, Step Functions, Azure Data Factory, etc. Database Architecture: Understand and optimize for different storage engines—relational (PostgreSQL, MySQL), columnar (Redshift, BigQuery), indexing engines (ElasticSearch), key-value stores (DynamoDB, Redis), Object stores (S3 or similar), and caching layers. Streaming Data Processing: Work with Apache Kafka (or similar platforms) to handle high-volume, low-latency data streams. Workflow Orchestration: Utilize Apache Airflow (or equivalent) to schedule and monitor complex data workflows. AI/ML Integration: Collaborate with data scientists to integrate LLMs and ML models into pipelines for inference, tagging, enrichment, or intelligent routing of data. Required Skills and Qualifications Active
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