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JTEC Consulting•Chantilly, VA

6d•Onsite

About The Position

JTEC Consulting LLC is seeking an experienced Data and Software Engineer to join a focused, agile engineering team building high-throughput, mission-critical data flows for a custom enterprise application. The Data and Software Engineer will design, implement, and optimize end-to-end data architectures, robust ETL/ELT pipelines, and cloud-native infrastructure at scale. Qualified candidates will demonstrate advanced software engineering fundamentals in Python with deep distributed systems expertise, rigorous data governance standards, and modern Lakehouse tooling. A current security clearance with agency-specific polygraph is strictly required for consideration.

Requirements

U.S. citizenship
Current TS/SCI with agency-specific polygraph required for consideration
Bachelor’s degree in Computer Science, Information Systems, or a related technical discipline (equivalent years of direct experience considered in lieu of a degree)
5+ years of progressive experience in data engineering, software engineering, or distributed backend systems
Effective written and verbal communication skills to support a collaborative, cross-functional team including engineering peers and operational stakeholders
Demonstrated ability to self-direct and solve complex performance bottlenecks in production distributed environments
Prior experience delivering platforms within federal, defense, or highly regulated enterprise spaces
Advanced proficiency in Python, including standard scientific and data manipulation libraries (Pandas, NumPy)
Robust Bash scripting skills for system automation, container startup sequences, and utility pipelines
Professional familiarity with Java for troubleshooting or extending distributed data systems
Deep production experience configuring, deploying, and tuning Apache Spark / PySpark jobs
Hands-on experience with modern Lakehouse formats (Apache Iceberg) and distributed SQL engines (Trino)
Strong AWS ecosystem expertise, specifically Amazon S3, AWS Lambda, and AWS Step Functions
Infrastructure as Code (IaC) deployment using Terraform or AWS CloudFormation
Containerization expertise using Docker or Podman
Advanced SQL capabilities with a proven record of schema design and performance tuning in MySQL and PostgreSQL
Hands-on experience with NoSQL document or key-value stores (e.g., Amazon DynamoDB)
Direct working experience manipulating geospatial data leveraging PostGIS and hexagonal hierarchical spatial index frameworks (H3)
Experience implementing data catalogs and governance tools such as Unity Catalog OSS or Apache Polaris
Track record implementing data observability or lineage using OpenLineage
Experience configuring data visualization layers and business intelligence dashboards using Apache Superset
Version control mastery (Git) and CI/CD automated deployment pipelines
Solid understanding of data security, privacy principles, role-based access control (RBAC), and regulatory compliance standards

Responsibilities

Architect, build, and maintain production-grade data pipelines and ETL/ELT workflows using Python, Apache Spark, and workflow orchestration frameworks (e.g., Apache Airflow)
Deploy and optimize open table formats (Apache Iceberg) and federated query engines (Trino) to support low-latency analytical queries and large-scale data processing
Design relational schemas, maintain indexes, and execute query optimization on MySQL and PostgreSQL for complex analytical workloads
Ingest, structure, and analyze non-traditional and spatial data formats utilizing indexing systems and spatial extensions like Uber H3 and PostGIS
Containerize microservices and batch jobs using Docker or Podman, provisioning and managing scalable AWS resources (S3, Lambda, Step Functions) via Terraform or CloudFormation
Implement cataloging and end-to-end operational lineage tracking using Unity Catalog OSS, Apache Polaris, and OpenLineage to satisfy strict compliance, privacy, and security mandates
Collaborate with data science and engineering peers to integrate AI/ML services and operationalize inference models into upstream and downstream data workflows
Lead technical strategy for large-scale data migrations, schema evolutions, and platform modernization initiatives with minimal system downtime
Translate ambiguous operational requirements into technical specifications with minimal oversight; author comprehensive technical documentation, establish testing standards, and mentor team members on data engineering design patterns