Unified Data Management Development Lead

The Leidos Digital Modernization Sector has a career opportunity for a Unified Data Management Development Lead supporting the Global Solutions Management - Operations II (GSM-O II) contract with the Defense Information Systems Agency (DISA). This contract supports the Operations, Sustainment, Maintenance, Repair, and Defense of the Defense Information System Network (DISN) within the DoD Information Network (DODIN), as well as mission modernization efforts that improve operational effectiveness, resiliency, and service delivery for DISA and its mission partners.

The Unified Data Management Development Lead will serve as a senior technical leader responsible for developing and leading execution of the project data strategy across the Global Management System (GMS) environment. This role will shape and deliver the data architecture, data lake and lakehouse patterns, streaming and batch data pipelines, data sharing capabilities, data catalog and governance approach, and mission-focused data products that enable enterprise situational awareness, observability, predictive analytics, automation, and future self-healing network capabilities.

The successful candidate will lead hands-on development and implementation across a complex AWS IL5/IL6 system-of-systems that collects, normalizes, correlates, enriches, shares, and visualizes large volumes of network, cyber, performance, configuration, event, and operational data. The role requires a strategic and flexible mindset to maximize the value of existing program investments, including Confluent Kafka, Elastic Stack, Databricks, AWS, ServiceNow, IBM Watson AIOps, OpenShift/Kubernetes, Bitbucket, Jira, Confluence, and automated CI/CD pipelines, while also defining the target-state architecture, roadmap, processes, tools, techniques, and development practices needed to take the project into the future.

This is a senior individual-contributor/technical lead role that will work closely with the Data Driven Operations Manager, Chief Engineer, Chief Architect, product owners, development teams, network engineers, cyber teams, operations users, vendors, and Government stakeholders. The UDM Development Lead will translate mission needs into executable technical plans, lead solution design and development, mentor engineers, drive technical decision-making, brief senior stakeholders, and ensure the data platform delivers measurable business and mission value while meeting DoD cybersecurity, compliance, performance, reliability, and cost objectives.

Primary Responsibilities

The successful candidate will:

• Lead the technical strategy, roadmap, architecture, development, and implementation of Unified Data Management capabilities across the GMS data ecosystem.
• Define the current-state and target-state data architecture for a secure, scalable, cloud-based data platform spanning AWS IL5/IL6, Confluent Kafka, Elastic Stack, Databricks, ServiceNow, Watson AIOps, and related data and automation tools.
• Architect and lead implementation of data lake, lakehouse, streaming, and batch-processing patterns for structured, semi-structured, and unstructured operational data.
• Lead design and development of data pipelines that ingest, normalize, correlate, enrich, route, retain, index, catalog, and share data from network, cyber, service management, telemetry, and automation sources.
• Maximize the use of existing data tools and technologies in the environment while evaluating new tools, integration patterns, and modernization approaches based on business value, mission value, total cost of ownership, security, operational supportability, and technical fit.
• Lead the design and implementation of data sharing capabilities across the organization, including data catalog, metadata management, data lineage, schema management, data quality, data contracts, and secure access control patterns.
• Design and guide implementation of mission-focused data products that support network operations, cyber operations, customer impact analysis, root-cause analysis, predictive analytics, auto-ticketing, automation, and self-healing network capabilities.
• Partner with AI/ML and AIOps stakeholders to ensure data is prepared, governed, and delivered in ways that enable anomaly detection, event correlation, predictive analytics, LLM-enabled troubleshooting, automated remediation, and operational decision support.
• Lead data integration across disparate systems using middleware, APIs, message brokers, Kafka topics/connectors, ETL/ELT components, data models, interface control documents, and system design artifacts.
• Optimize cloud architecture and cost for data workloads, including compute, storage, retention, indexing, partitioning, throughput, lifecycle management, observability, and scaling strategies.
• Guide and mentor engineers, developers, analysts, and testers; help manage technical backlog, team capacity, workload prioritization, code quality, design reviews, and delivery commitments in a fast-paced Agile environment.
• Integrate data capabilities into program DevSecOps processes, automated CI/CD pipelines, infrastructure as code, configuration as code, automated testing, data simulators, and controlled deployment workflows.
• Develop and maintain technical artifacts, including architecture decision records, logical and physical data architectures, data flow diagrams, system design documents, interface documents, implementation plans, test strategies, user acceptance strategies, and operational transition plans.
• Lead troubleshooting and optimization of complex data flows, pipeline reliability, data quality issues, query performance, system integration issues, network data anomalies, and platform performance constraints.
• Ensure solutions comply with DoD cybersecurity, data governance, access control, encryption, auditability, RMF, DISA STIG, and classified/unclassified environment requirements.
• Collaborate with product owners, architects, operations leaders, network engineers, cyber stakeholders, vendors, and Government customers to define requirements, resolve dependencies, brief solution approaches, demonstrate capabilities, and align delivery with strategic program goals.
• Support Engineering Review Board activities, technology trade studies, analysis of alternatives, ROMs, proposal inputs, roadmap planning, and modernization recommendations for new and enhanced GSM-O capabilities.
• Maintain a forward-looking perspective on emerging data engineering, cloud, AI/ML, AIOps, data governance, automation, and network operations technologies, and recommend pragmatic adoption paths that improve mission outcomes.

Kibana Syslog Ansible Jenkins Polling Planning Auditing Equities Topology Indexing Logstash Dashboard Telemetry OpenShift Bitbucket DevSecOps Confluent Cloudbees Pipelines Operations Leadership Management Automation Governance Resilience Kubernetes ServiceNow Data Lakes Middleware Encryption Databricks AWS Devops Scalability Market Data Artifactory Apache NiFi AWS Kinesis Data Quality Data Sharing Apache Kafka Unity Engine Data Lineage Apache Flink Microservices Observability Data Security Data Modeling Data Exchange Data Strategy Elasticsearch Cyber Security Prioritization Systems Design Cloud Services Data Pipelines Data Retention Test Automation Decision Making Access Controls Data Governance Data Management Solution Design Network Routing Sprint Planning Ancient History Computer Science Data Integration Machine Learning Data Engineering Cyber Operations Secret Clearance Command Controls Batch Processing Agile Methodology Docker (Software) CompTIA Security+ Data Architecture Global Management Computer Networks Query Performance Unstructured Data Data Flow Diagram Anomaly Detection Amazon CloudWatch Stream Processing Business Valuation Security Clearance Service Management Naming Conventions Data Preprocessing Technical Strategy Root Cause Analysis Systems Engineering Systems Integration Amazon Web Services Elastic (ELK) Stack Information Systems Incident Management Metadata Management Networking Hardware Feature Engineering Software Engineering Lifecycle Management Predictive Analytics Architectural Design Atlassian Confluence IT Service Management Operations Management Solution Architecture Cost Benefit Analysis Operational Data Store Distributed Data Store Artificial Intelligence Technical Documentation Border Gateway Protocol Cyber Security Policies Total Cost Of Ownership Cybersecurity Compliance Requirements Traceability SQL (Programming Language) Software Quality (SQA/SQC) IAT Level II Certification Java (Programming Language) Verbal Communication Skills Git (Version Control System) Extract Transform Load (ETL) Infrastructure as Code (IaC) Juniper Network Technologies Amazon Elastic Compute Cloud Cloud Computing Architecture Python (Programming Language) Multiprotocol Label Switching Architecture Decision Records Identity And Access Management IBM Tivoli Management Framework Microsoft Certified Professional Troubleshooting (Problem Solving) Simple Network Management Protocols Application Programming Interface (API), * Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Systems Engineering, Information Systems, or a related technical discipline with 8+ years of relevant experience; Master’s degree with 6+ years; or equivalent additional experience in lieu of degree. Final experience requirement should be validated against the selected Leidos job profile.

• Demonstrated experience leading strategy, architecture, design, development, and delivery of data-driven capabilities in complex enterprise or mission environments.
• Hands-on experience with modern data architectures, including data lakes or lakehouses, distributed data platforms, streaming data architectures, ETL/ELT pipelines, data integration, data modeling, and structured/semi-structured/unstructured data processing.
• Strong hands-on knowledge of Confluent Kafka or Apache Kafka, including topics, partitions, connectors, schema management, stream processing, and operational troubleshooting.
• Experience with Elastic Stack, including Elasticsearch, Logstash, Kibana, indexing strategies, dashboarding, observability, data retention, and performance tuning.
• Experience with AWS cloud services relevant to data platforms, such as EC2, S3, IAM, KMS, CloudWatch, Lambda, Kinesis, networking, storage, and cost optimization; AWS GovCloud or DoD cloud experience preferred.
• Experience designing or implementing secure data sharing, data catalog, metadata management, schema management, lineage, governance, and data quality capabilities.
• Experience developing, integrating, and supporting middleware, APIs, message brokers, microservices, and data exchange patterns across disparate systems.
• Software development experience in Python, Java, SQL, or comparable languages used for data engineering, automation, and platform integration.
• Experience with containers and orchestration platforms such as OpenShift, Kubernetes, Docker, and related deployment patterns.
• Experience with DevSecOps, CI/CD pipelines, Git/Bitbucket, Jenkins/CloudBees or equivalent tools, Artifactory, Ansible, infrastructure as code, configuration as code, automated testing, and controlled release processes.
• Working knowledge of Agile delivery practices, backlog refinement, sprint planning, technical story decomposition, acceptance criteria, Jira, and Confluence.
• Ability to develop and review technical documentation, requirements traceability, detailed plans and schedules, software/system engineering documents, interface documents, and operational transition artifacts.
• Strong understanding of cybersecurity principles, encryption, access controls, identity and access management, least privilege, auditing, and secure design for classified and unclassified environments.
• Working knowledge of DISA STIGs, RMF, DoD cybersecurity policies, and DoD compliance frameworks, or demonstrated ability to apply similar compliance requirements in secure mission environments.
• Proven ability to lead technical teams without requiring direct reporting authority, mentor engineers, facilitate design decisions, resolve complex technical problems, and align technical execution to business and mission outcomes.
• Strong written and verbal communication skills with the ability to brief technical solutions, risks, tradeoffs, status, and recommendations to customers, senior leadership, architects, engineers, operators, and non-technical stakeholders.
• Ability to obtain and maintain a DoD Secret security clearance; active Secret clearance preferred for start.
• Ability to obtain Security+ or equivalent DoD 8570 IAT II certification within the program-required timeframe after start., * Experience supporting DISA, DISN, DODIN, GSM-O, GMS, or other DoD network operations, cyber operations, or mission command/control environments.
• Deep understanding of network operations data, including event, performance, configuration, syslog, SNMP polling/traps, telemetry, topology, ticketing, customer impact, and incident management data.
• Experience with tools or data sources such as DXNetOps, Netscout, Netcool, Watson AIOps, Juniper Northstar, CTPView, Cisco EPNM, Ciena MCP, Silvx, Cisco Prime Optical, ServiceNow ITSM/TSOM/TNI/OMT, Palantir, Cisco NSO, and Ansible.
• Experience with Databricks, Spark, Delta Lake, Unity Catalog or comparable data catalog/governance capabilities, Apache NiFi, Apache Flink, Kafka Streams, ksqlDB/KSQL, or similar data engineering technologies.
• Experience architecting data products that support observability, situational awareness, predictive analytics, anomaly detection, event correlation, root cause analysis, customer impact analysis, auto-ticketing, and automated remediation.
• Familiarity with AI/ML, AIOps, NLP, LLM-enabled operational support, feature engineering, model-ready data preparation, and governance of data used for machine learning workloads.
• Experience optimizing Elastic, Kafka, Databricks, or cloud data platforms for throughput, latency, cost, retention, indexing, storage lifecycle, high availability, resiliency, and operational supportability.
• Experience designing solutions that move or replicate data across security boundaries, including IL5/IL6 patterns, cross-domain transfer concepts, diode-based transfer patterns, and secure data consolidation.
• Experience developing data standards, reference architectures, reusable pipeline patterns, data contracts, naming conventions, and governance processes that improve enterprise reuse and accelerate delivery.
• Experience developing dashboards, visualizations, and operational data products in Kibana or comparable tools.
• Experience with network fundamentals, routing and switching concepts, BGP, MPLS, optical networking, network device configurations, and transport protocols.
• Experience with Engineering Review Boards, architecture governance, analysis of alternatives, trade studies, cost-benefit analysis, proposal/ROM support, and customer demonstrations.
• Relevant certifications such as AWS Solutions Architect, AWS Data Engineer, AWS DevOps Engineer, Certified Kubernetes Administrator, Confluent/Kafka, Elastic, Databricks, Security+, CISSP, or comparable technical certifications.

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Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit www.Leidos.com .