• Lead the full lifecycle of data engineering projects, from initial requirement gathering with stakeholders to production deployment and monitoring • Design, develop and maintain complex data transformations, ensuring high data quality and performance using scripting languages like Python, Airflow, DBT and databases such as Snowflake or similar Data Lakes • Build, scale, and maintain automated workflows using Apache Airflow to manage sophisticated data dependencies • Maintain high engineering standards through CI/CD implementation and rigorous version control using GitHub • Implement automated processes for data validation, ensuring high standards of data quality, accuracy, and integrity across all pipelines • Act as a technical partner to the Analytics, Sales, and Marketing teams, building curated datasets that drive strategic decision-making

Do you want to design and scale enterprise data platforms that power analytics, AI, and real business impact? Ready to lead architectural decisions across complex, high-impact data environments? Then this role is for you. At Nimble Gravity, we turn complex data into actionable insight—and that starts with building scalable, high-performing data platforms. We’re seeking a Lead Databricks Architect who is passionate about designing and operating modern data ecosystems, driving platform excellence, and enabling organizations to unlock the full value of their data. If you thrive at the intersection of data architecture, platform ownership, and hands-on technical execution, and enjoy leading high-impact initiatives in complex environments, then you belong with us. Enterprise Data Architecture & Modernization - Act as the senior technical authority for enterprise data architecture across client engagements. - Design and evolve cloud-native, best-of-breed data platforms for our clients, supporting analytics, reporting, automation, and AI at enterprise scale, with a strong focus on Databricks as a core platform. - Define target-state architectures for data ingestion, transformation, storage, semantic layers, and consumption across federated domains. - Establish and enforce architectural standards for scalability, performance, security, resiliency, and cost efficiency, including platform-level governance and operational best practices. Modern Data Pipelines & Platforms - Architect and guide the implementation of modern data pipelines using tools such as Databricks and dbt. - Design ELT and streaming patterns that are testable, observable, and resilient. - Define data modeling standards (analytical, dimensional, and semantic) that enable trusted self-service analytics. - Partner with client data engineering teams to ensure consistent application of patterns and reuse of shared platform capabilities, including effective use and management of Databricks environments. AI-Ready & Semantic Data Foundations - Design AI-ready data foundations, including semantic layers, contextual metadata, and reusable data contracts. - Enable machine learning and generative AI use cases by ensuring data is well-modeled, discoverable, governed, and explainable. - Collaborate with client Data Science and Analytics teams to support feature stores, experimentation environments, and reusable data products. - Advance semantic modeling and context engineering to enable natural-language analytics and AI-driven insights. Data Governance, Quality & Trust - Partner with client Data Governance stakeholders to define standards for data quality, metadata management, lineage, and stewardship. - Ensure architectural designs support regulatory and compliance requirements (e.g., SOX, SEC, FINRA, GDPR, DORA). - Promote reuse, interoperability, and consistent definitions of critical enterprise data across client environments, including alignment with platform governance models. Databricks Platform Ownership & Administration - Own and administer Databricks workspaces across client environments, including configuration, access controls, and governance (Unity Catalog, IAM, RBAC). - Manage cluster configurations, job orchestration, and cost optimization to ensure efficient and reliable platform operations. - Monitor platform performance, troubleshoot issues, and ensure SLAs for data pipelines and workloads. - Establish best practices for CI/CD, environment management, and operational support within Databricks. - Partner with client teams to operationalize and scale Databricks as a core enterprise data platform. Qualifications Education & Certificates - Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related fields. - Cloud or data architecture certifications (AWS, data management, analytics) preferred. Professional Experience - 10+ years of experience in data architecture, data engineering, or enterprise analytics. - Proven experience designing and operating modern, cloud-native data platforms. - Hands-on expertise with AWS-based data ecosystems and modern analytics stacks. - Strong experience building data platforms that support AI, machine learning, and advanced analytics. - Experience operating in federated data models and complex enterprise environments. - Strong hands-on experience with Databricks, including workspace setup, configuration, administration, and platform optimization; experience with AWS setup and administration is strongly preferred. Competencies & Attributes - Deep technical fluency combined with strong business acumen. - Ability to translate strategy into executable architectural designs. - Pragmatic, delivery-oriented mindset with strong attention to data quality and sustainability. - Collaborative, trusted partner to senior client stakeholders and technical teams. - Comfortable operating in high-visibility, transformational initiatives. Nimble Gravity is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, state, or local law. Nimble Gravity considers all qualified applicants. This position is open only to candidates who are legally authorized to work in the United States. We do not provide visa sponsorship for this role.

• Design and implement scalable Data Lakehouse architectures on AWS • Build and optimize data warehouse solutions using Amazon Redshift and Snowflake • Develop and manage ETL/ELT pipelines using PySpark and AWS Glue • Design Fact and Dimension data models for analytics workloads • Optimize SQL queries, data distribution, and partition strategies • Process and transform structured and semi-structured data using PySpark • Manage data transformation workflows using DBT • Ensure data quality, performance, and reliability across data pipelines • Work with BI tools such as Tableau or Power BI for reporting and analytics • Collaborate with data engineers, analysts, and business teams to deliver scalable data solutions

• Own the design and development of features and components for the data platform, focusing on high-throughput data ingestion, transformation, and storage. You will report to the Director, Engineering. • Architect and implement robust, distributed, and scalable data processing pipelines in Go to ensure data quality and reliability. • Contribute to the technical strategy and roadmap for the data platform, anticipating future data needs for product features and internal analytics. • Mentor junior and mid-level engineers on the team, and conduct thorough code reviews to ensure quality and best practices. • Participate in an on-call rotation to address urgent operational issues impacting data services.