Principal Databricks Architect

MetaPhase Consulting

1d•Remote

About The Position

At MetaPhase, we believe Quirky is Cool and being authentic is the only way to be! We take the work we do very seriously and do a lot of important mission-focused work for our clients. We are individuals with different passions and strengths who take as much joy in the work we do as from those we work with. Today, we have a team that is invested in creating new solutions that lean forward, challenge the status quo, but also reflect our intimate knowledge of our customers’ business. Over the years we have fostered a culture in which we are united by shared values—passion, solidarity, generosity, curiosity, and boldness—and these come alive in the work we do and how we do it. Together, we know our people are our difference—for our clients and our colleagues. Are you ready to: Work alongside a dedicated and diverse set of people to offer honest advice and practical guidance to our clients? Learn and grow by taking advantage of every opportunity available to you? Join a company which prides itself on its shared values and inclusive culture? Be the difference and make it happen? The Principal Databricks Architect / Engineer will provide technical leadership for the architecture, design, implementation, governance, and sustainment of a Databricks-based data and analytics environment. This role serves as the senior technical authority for the platform, translating mission and business needs into secure, scalable, maintainable, and cost-effective data solutions. The Principal Databricks Architect / Engineer will establish technical standards, advise and guide engineers through complex implementation decisions, and implement Databricks architectural best practices so the environment is operated in accordance with data governance, security, reliability, and performance requirements as well as agency IT and security policies.

Requirements

Bachelor’s degree in a technical discipline and 8+ years of progressive experience designing, building, and operating cloud data, analytics, or enterprise data platforms.
5+ years of hands-on data engineering experience using Python, SQL, Apache Spark, and production ETL/ELT pipelines, including testing, deployment, monitoring, and troubleshooting.
3+ years of hands-on Databricks architecture and engineering experience, such as Delta Lake, Unity Catalog, workflow orchestration, performance tuning, and production support
Current Databricks Certified Data Engineer Professional certification required.
Demonstrated ability to lead technical architecture, establish engineering and governance standards, and communicate solution decisions to technical and nontechnical stakeholders
Ability to obtain a U.S. Public Trust suitability determination
U.S. Citizenship Required(Clearance / Citizenship Requirements)

Nice To Haves

Experience implementing enterprise data governance and security controls, including role-based access, metadata, lineage, auditability, data-quality management, and sensitive-data protection.
Experience with Databricks Asset Bundles, Databricks CLI or APIs, infrastructure-as-code, CI/CD, and automated environment promotion.
Experience architecting data integrations across cloud services, enterprise systems, APIs, analytics platforms, and batch or streaming data sources.
Relevant AWS, Azure, or Google Cloud architect or data-engineering certification; additional advanced Databricks certification is a plus.
Experience supporting regulated, public-sector, or security-sensitive environments and leading technical teams using Agile methodologies.
Additional Databricks certifications (e.g., Databricks Machine Learning Engineer Associate or Professional, Databricks Generative AI Engineer Associate)

Responsibilities

Lead the target architecture and technical roadmap for the Databricks platform, including data ingestion, storage, transformation, orchestration, analytics, sharing, and consumption patterns.
Design and govern scalable Lakehouse architectures using Databricks capabilities such as Delta Lake, Unity Catalog, Databricks SQL, Lakeflow Jobs, Auto Loader, and Apache Spark
Define and maintain platform standards for data modeling, medallion architecture, pipeline design, code quality, environment promotion, CI/CD, testing, documentation, observability, and incident response.
Lead the design and implementation of enterprise data governance, including cataloging, access controls, data classification, lineage, auditability, retention, data quality, and stewardship processes.
Architect secure integrations between Databricks and source systems, cloud services, enterprise applications, APIs, data warehouses, and reporting or visualization tools.
Provide technical leadership and hands-on engineering support for complex data pipelines, batch and streaming workloads, performance tuning, troubleshooting, and releases.
Review technical designs, code, and deployment artifacts; mentor engineers; and elevate team capability through reusable patterns, documentation, and knowledge transfer
Support architecture reviews, technical demonstrations, operational reporting, backlog refinement, and continuous improvement activities.