Data Development Lead

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. Basic Qualifications - 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. - 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. - Experience designing or implementing secure data sharing, data catalog, metadata management, schema management, lineage, governance, and data quality capabilities. - 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. - Strong understanding of cybersecurity principles, encryption, access controls, identity and access management, least privilege, auditing, and secure design for classified and unclassified environments. - 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.