Lead Data Architect

Steampunk•McLean, VA

1d•$165,000 - $210,000

About The Position

We are seeking a Principal Data Solution Architect / Lead Data Architect to serve as the senior-most technical authority for end-to-end data architectures, cloud data platforms, integration strategies, and enterprise-scale modernization initiatives. This role operates at the intersection of architecture, engineering, analytics, AI, and mission strategy. The Principal Data Solution Architect shapes the vision for how data is collected, governed, transformed, stored, accessed, and activated across complex environments—ultimately enabling reliable analytics, ML/AI capabilities, and mission-critical applications. This leader drives architectural strategy across data platforms, lakehouses, warehouses, integration layers, and AI/ML pipelines, while guiding engineering teams toward scalable, secure, and maintainable solutions. You will contribute to the growth of our AI & Data Exploitation Practice!

Requirements

Ability to hold a position of public trust or higher clearance as required.
Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Engineering, Information Systems, Cloud Architecture, or a related technical field.
10+ years of professional experience designing enterprise-scale data solutions, with significant architectural leadership.
Expert-level knowledge of modern data architectures including lakehouse, data mesh, data fabric, MDM, event-driven architectures, and domain-driven design.
Deep experience with cloud-native data ecosystems across AWS, Azure, or GCP—including storage, compute, orchestration, serverless, virtualization, containerization, and security services.
Mastery of distributed data processing frameworks (e.g., Spark, Databricks, Flink, Kafka, Synapse, Dataflow) and strong proficiency in SQL and Python.
Proven ability to design end-to-end ingestion pipelines, transformation logic, metadata systems, feature stores, and data serving layers optimized for analytics and AI workloads.
Understanding of enterprise data governance, including cataloging, lineage, privacy, tagging, Zero Trust access patterns, and compliance requirements.
Strong communication skills with the ability to influence senior stakeholders, justify architectural decisions, and translate complex concepts into actionable insights.
Demonstrated success mentoring technical staff, leading architecture reviews, and guiding multi-team delivery across complex modernization programs.
Experience collaborating with Data Science, AI/ML, and MLOps teams to ensure data architectures support scalable model development and operationalization.

Nice To Haves

AWS Data Analytics Specialty
AWS Solutions Architect – Professional
Azure Solutions Architect Expert
Databricks Data Engineer Professional
Google Professional Data Engineer
Snowflake SnowPro Advanced Architect
TOGAF or cloud architecture frameworks
Local to Washington, DC metro area

Responsibilities

Serve as the chief architect for data platform modernization, designing enterprise data ecosystems including lakehouse architectures, data mesh/fabric patterns, domain modeling, and multi-cloud data strategies.
Translate mission and business needs into actionable data architecture roadmaps, reference architectures, and solution blueprints.
Architect robust ingestion, transformation, and serving layers using a blend of batch, streaming, CDC, API-based, and event-driven patterns.
Lead end-to-end data modeling strategy, including canonical data models, semantic layers, MDM architectures, metadata systems, and AI/ML-aligned feature modeling.
Partner with Data Engineering, Data Science, AI/ML Engineering, and LLMOps/MLOps teams to ensure data platforms support analytics, ML, RAG systems, and advanced automation use cases.
Define and enforce enterprise data standards: schema evolution, data contracts, quality frameworks, lineage expectations, observability, data zones, and Zero Trust data-access policies.
Drive platform engineering decisions including storage optimization, cluster sizing, compute orchestration, network design, and cost-performance tradeoffs.
Guide selection and adoption of platform technologies such as Databricks, Snowflake, Redshift, Synapse, BigQuery, lakehouse engines, metadata platforms, and orchestration tools.
Oversee architectural governance, including design reviews, performance evaluations, cloud readiness assessments, and alignment with enterprise cybersecurity and compliance requirements.
Serve as a senior advisor to client executives, framing modernization strategies, defining investment pathways, and articulating value realization for enterprise data initiatives.
Mentor Data Engineers, Data Architects, and cross-functional technical staff, strengthening architectural maturity across programs.
Develop reusable frameworks, architectural patterns, playbooks, and internal accelerators that improve consistency and reduce delivery time across engagements.
Stay current with emerging trends in cloud-native data ecosystems, metadata automation, distributed compute, AI-ready data architectures, and federal data regulations.