Data Engineering Software Developer

Join Leidos' Digital Sector as we stand up a brand-new Agile development team at the forefront of federal IT modernization, delivering real digital transformation where it matters most. This is a rare ground-floor opportunity to architect, build, and integrate cutting-edge infrastructure solutions in support of some of our nation's most sensitive and critical missions.

What to expect:

Early-stage flexibility with partial telework during initial development phases

Full-time on-site presence at secure facility in Washington, DC will be required as the program maturesA collaborative, Agile environment where your input shapes the architecture from day oneDev platform based on Service Now, Elastic, Kafka, AWS and Red Hat What  you'll need:

An active TS/SCI clearance (required for consideration)
Willingness to complete and maintain additional customer suitability screenings throughout employment

Current Security+ certification or higher/equivalent

Primary Responsibilities:

Designs, builds, and maintains scalable, reliable data systems and pipelines to ingest, process, and transform large, complex, and disparate data sources for analytics and reporting.

Unifies and integrates data from multiple network operations systems into a consistent, accessible platform, ensuring data quality, accuracy, security, and compliance with DoD and DISA requirements.

Implements solutions using Confluent Kafka, Elastic Stack, AWS, and Red Hat Open Shift (Kubernetes), including creating Kafka, Elastic, and Logstash pipelines and supporting Kibana visualizations and dashboards with React, JavaScript, and HTML.Develops and manages data warehouses and data lakes to support analytics, reporting, and operational dashboards, optimizing infrastructure for performance, scalability, and cost-effectiveness through automation and best practices.

Continually analyze and optimize ingestion throughput, query performance, indexing strategies, and storage efficiency across Elastic and Kafka platforms.

Automates data-related tasks such as ETL/ELT processes, data validation, cleansing, monitoring, and supports CI/CD pipelines for automated build, test, and deployment.

Collaborates with data analysts, visualization engineers, and other stakeholders to deliver integrated, actionable data solutions, and documents data architecture, integration patterns, and operational procedures.

Troubleshoots and resolves issues related to data ingestion, processing, and platform performance, storage, memory, partitioning, and cluster performance.

Basic Qualifications:

Bachelor’s degree in Computer Science, Engineering, or related technical discipline and 8+ years of relevant experience.

An active TS/SCI clearance

Willingness to complete and maintain additional customer suitability screenings throughout employment

Current Security+ certification or higher/equivalent

Hands-on experience designing, building, and maintaining data pipelines (ETL/ELT) and integrating multiple, disparate data sources.

Experience with data warehouse and/or data lake technologies (e.g., AWS Redshift, S3, Hadoop, Snowflake, or similar).Proficiency with data engineering tools and languages such as Python, Java, SQL, and shell scripting.

Experience with Elastic Stack (Elasticsearch, Logstash, Kibana) and/or Kafka for data ingestion and processing.

Familiarity with network operations data (logs, metrics, events, ticketing, CMDB) and related data models.

Experience with Linux/UNIX system administration and automation.

Knowledge of data security, access controls, and compliance frameworks (DoD, DISA, RMF, STIGs).

Experience with CI/CD pipelines, containerized pipelines, version control (Git, Bitbucket), and Dev Ops practices.

Strong communication and documentation skills for collaboration and knowledge sharing.

Preferred Qualifications:

Experience with DISA, DISN, or other DoD network environments, including developing and deploying software applications that meet DoD security standards (e.g., STIGs), and knowledge of network security, encryption, and access controls in classified environments.

Experience with big data technologies and platforms such as Kafka, Spark, NiFi, Hadoop, Elasticsearch, Logstash, Databricks, and ELK Stack for text mining, summarization, search, and entity extraction.

Experience with cloud-based data platforms (AWS, Azure, GCP, AWS Gov Cloud), cloud-integrated platforms, and infrastructure as code, including networking and security policies.

Familiarity with Kubernetes deployment, platform upgrades, patching,…