• Own the full lifecycle from raw video ingestion to the decisions made by autonomous AI agents in production. • Shape the future of agentic AI and develop innovative solutions that drive millions in revenue today. • Collaborate with a team to push forward cutting-edge AI technology.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description 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. Contributions - 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. Qualifications - Ability to hold a position of public trust or higher clearance. - 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. Requirements - Preferred certifications (highly relevant): - 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. Benefits - The projected compensation range for this position is $165,000 to $210,000. - Annual salary is just one aspect of Steampunk’s total compensation package for employees. Company Description Steampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. Steampunk is a Change Agent in the Federal contracting industry, bringing new thinking to clients in the Homeland, Federal Civilian, Health and DoD sectors. Through our Human-Centered delivery methodology, we are fundamentally changing the expectations our Federal clients have for true shared accountability in solving their toughest mission challenges. As an employee-owned company, we focus on investing in our employees to enable them to do the greatest work of their careers – and rewarding them for outstanding contributions to our growth. We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Steampunk participates in the E-Verify program.

• Design resilient and well-modeled data layers with clear interfaces and versioning • Improve data quality at scale, build modular and reusable packages and instrument pipelines for rich telemetry • Identify scalability problems and constraints in our products, and proactively work to improve them • Drive and introduce initiatives across our data team to improve developer experience and develop new data capabilities • Create and maintain the most reliable, secure, performant and high throughput service for our customers by using cutting-edge cloud technology

• Migrate data and analytics workloads from BigQuery to Snowflake • Support GCP to AWS data platform migration • Develop and optimize ETL/ELT pipelines using Python and SQL • Build analytics-ready datasets for reporting and dashboards • Support BI tools such as Looker, Amazon QuickSight, or Tableau • Ensure data quality, performance, and reliability • Collaborate with data architects, analytics, and DevOps teams