Senior Manager, Data Engineering

Position Overview

We are seeking an experienced Chief Engineer to provide technical leadership and architectural oversight for a large-scale enterprise data lake and data brokerage platform supporting a Department of Defense (DoD) mission.

The Chief Engineer will be responsible for defining the overarching technical architecture, ensuring integration across multiple engineering disciplines, and guiding the development of a secure, scalable, and highly available data ecosystem.

The ideal candidate possesses a broad understanding of Platform Engineering, Cloud Infrastructure, Data Engineering, and Data Science, with the ability to translate complex mission requirements into cohesive technical solutions.

This is a strategic technical leadership position requiring strong systems-level thinking, architectural decision-making, and the ability to guide specialized engineering teams.

Key Responsibilities

• Enterprise Architecture: Lead the design and evolution of an enterprise-scale data platform supporting data ingestion, storage, processing, discovery, analytics, and distribution.
• Technical Leadership: Provide architectural direction and technical oversight across Platform Engineering, Cloud Infrastructure, Data Engineering, Data Science, and Software Development teams.
• Systems Integration: Ensure interoperability across distributed systems, cloud environments, data pipelines, APIs, and mission applications.
• Architecture Governance: Establish technical standards, design principles, and engineering best practices to ensure scalability, reliability, security, and maintainability.
• Solution Development: Evaluate architectural alternatives, identify technical risks and dependencies, and develop solutions aligned with mission objectives.
• Security & Compliance: Collaborate with cybersecurity and compliance stakeholders to incorporate appropriate security controls, data governance, and regulatory requirements into system architecture.
• Technical Roadmapping: Support technical planning, capability prioritization, technology selection, and long-term platform modernization.
• Stakeholder Engagement: Communicate complex technical concepts, architectural decisions, tradeoffs, and risks to engineering teams, program leadership, and government stakeholders.

Required Qualifications

• 10+ years of experience in software engineering, systems engineering, cloud architecture, data engineering, or related technical disciplines, including significant experience leading complex technical initiatives.
• Understands the DoD Zero Trust Reference Architecture, and its application in the context of Iceberg/Trino.
• Understands technical considerations for operating Petabyte scale Iceberg warehouses including data pipelines, and data cataloging.
• Demonstrated experience architecting or overseeing large-scale, distributed enterprise data systems, including experience with end-to-end ownership of vendor data onboarding, ETL, storage, governance, and access layers.
• Hands-on depth with open-source ETL and data lakehouse technologies, including Apache NiFi (ingestion/routing/provenance), Apache Spark (processing/transformation), Apache Iceberg (storage), and Trino (query/access layer).
• Experience with OpenMetadata or comparable tools (such as Datahub, Alation, etc…) for cataloging, lineage, governance, and data quality metrics.
• Experience building usage analytics and Grafana dashboards that help customers measure the value and return on investment of individual vendor data feeds.
• Experience designing automated data quality evaluation processes that evaluate new feeds at onboarding and continuously monitor completeness, accuracy, timeliness, and schema stability.
• Strong understanding of fine-grained access control, including attribute-based and field-level security enforced by user entitlements, classification levels, and regulatory requirements.
• Experience deploying and operating containerized data platforms on Kubernetes across Azure and AWS, including infrastructure as code, CI/CD, high availability, and security hardening.
• Proven technical leadership across engineering teams, with the ability to set architectural standards and serve as the primary technical liaison to customer stakeholders.
• Broad technical understanding across the following disciplines with hands on experience with at least two:
• Platform Engineering: Container orchestration, Kubernetes, CI/CD, infrastructure automation, observability, and platform reliability.
• Cloud Infrastructure: Cloud-native architectures, networking, compute, storage, identity and access management, and infrastructure as code.
• Data Engineering: Data lakes, ETL/ELT pipelines, batch and streaming architectures, data integration, metadata management, and data governance.
• Data Science: Foundational understanding of machine learning, statistical analysis, AI/ML workflows, and the infrastructure required to support advanced platform and data analytics.
• Ability to develop and evaluate end-to-end technical architectures spanning multiple engineering domains.
• Strong understanding of system scalability, high availability, fault tolerance, security, and performance optimization.
• Demonstrated ability to lead technical design discussions, evaluate engineering tradeoffs, and guide multidisciplinary engineering teams.
• Excellent communication skills with the ability to engage both technical and nontechnical stakeholders.
• Experience with AWS, Azure, Kubernetes, Terraform, Apache NiFi, Apache Spark, Trino, or comparable technologies.
• Experience designing large-scale data lakes, data fabrics, data mesh architectures, or enterprise data brokerage systems.
• Experience supporting petabyte-scale data environments or high-throughput data processing systems.

Preferred Qualifications

• Hands-on engineering experience in one or more of the following: Platform Engineering, Cloud Engineering, Data Engineering, Software Engineering, or Data Science.
• Knowledge of DoD or Federal data governance policies, security requirements, and regulatory frameworks, including NIST, RMF, FedRAMP, and applicable DoD data management policies.
• Familiarity with cross-domain data transfer, data classification, access control, and sensitive data handling requirements.
• Experience supporting DoD, Intelligence Community, or other Federal enterprise technology programs.
• Experience evaluating emerging technologies and incorporating AI/ML capabilities into enterprise data architectures.

Ideal Candidate

The successful candidate is a technical generalist with deep architectural expertise and hands on experience in two of the areas above who understands how complex engineering disciplines interact to deliver an integrated enterprise capability.

They must possess a T-shaped architectural profile, combining broad cross-domain awareness with deep, practical acumen in at least one technical discipline to credibly guide high-level decisions. While not expected to perform daily hands-on troubleshooting, they must leverage hands-on experience to evaluate engineering tradeoffs, challenge assumptions, and lead overall system architecture.

Above all, this individual must be capable of seeing the entire technical ecosystem, understanding its dependencies, and providing the engineering leadership necessary to deliver a secure, reliable, and scalable enterprise data platform.