• Design, build and operate data and AI platforms as code, using Git‑based workflows as the source of truth. • Implement pull‑request‑driven change control, automated testing and CI/CD pipelines. • Define and maintain Infrastructure‑as‑Code for data and AI systems to ensure consistency, traceability, and rollback capability. • Design and maintain scalable ETL/ELT pipelines that support AI/ML model training and retraining, feature engineering and feature stores, batch and near‑real‑time inference workflows. • Support Retrieval‑Augmented Generation (RAG) and internal AI agents by curating, indexing and refreshing select data sources. • Implement proactive monitoring for data quality and schema integrity, pipeline performance and failure modes. • Partner with Product and Engineering teams to operationalize models by integrating data pipelines into MLOps workflows. • Build and operate Azure-based data and AI platforms, including storage, compute, orchestration and containerized services.

• Query, extract, and transform data from the data warehouse (BigQuery) to answer complex, multi-dimensional business questions spanning inventory, organic sales, traffic, sponsored ads, and DSP. • Understand the full schema of the data warehouse — which tables hold what data, how they relate to each other, and what joins are required for accurate cross-domain analysis. • Partner with internal teams to identify the most common and highest-value analytical questions clients and team members need answered. • Design and build automated data pipelines and queries that feed AI tools with clean, structured, and contextually appropriate data. • Develop and refine Claude Skills and Gemini Gems that enable conversational AI to conduct reliable eCommerce data analysis — working directly with prompt engineers to encode analytical methodologies into AI workflows. • Collaborate with prompt engineers to document best practices for data analysis (statistical methods, benchmarking approaches, trend identification) so that AI tools can replicate expert-level thinking. • Continuously improve and validate AI-generated outputs against manual analysis to ensure accuracy and trustworthiness. • Help automate recurring analytical workflows into chatbot-style interfaces that the broader team can use without SQL knowledge. • Identify operational bottlenecks and repetitive tasks across the business, then design and deploy AI agents to automate them — from data reporting to client deliverable generation to internal workflow management. • Build, test, and maintain AI agents using Claude (Claude Code, Claude Co-work) and Gemini, ensuring they perform reliably and are adopted by the team.

• 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.