Data and Analytics Engineer

Nimble Gravity helps financial institutions and other clients adopt AI to achieve measurable business outcomes. The Data and Analytics Engineer will build trusted data foundations, pipelines, data models, and semantic layers that enable reliable AI agents and natural language analytics. The role also involves data quality, governance, documentation, client communication, technical workshops, and production handoff.

Responsibilities:

• Architect flexible, performant data models that drive LOB team toward single sources of truth across their LOB business domains
• Use SQL, Python, dbt, and Snowflake to build and maintain data infrastructure for reporting, analysis, and automation
• Perform data QA and develop automated testing procedures for Snowflake data models
• Provide input into data governance strategies including permissions, data lineage, and data definitions
• Design Data security so that the model only has access to the data
• Build semantic data models that expose LOBs data to natural language queries via Cortex Analyst, turning complex schemas into something a business stakeholder and Clients can ask a question of
• Define and validate the metrics, dimensions, and relationships that AI agents need to reason correctly over LOBs data
• Identify and resolve gaps in data structure, naming, and coverage that would cause an agent to fail or produce incorrect results
• Documented playbooks, reusable data model templates, and semantic model libraries that can be maintained and extend
• Run technical workshops to upskill other team members
• Author semantic view configurations and skill files (YAML + Markdown) that a non-technical analyst can invoke in plain English

Requirements:

• Advanced SQL: CTEs, window functions, incremental pipeline patterns. You can write complex queries without referencing documentation
• Experience building data infrastructure involving large-scale relational datasets; strong instincts for pipeline design, QA, and testing across the full stack from ingestion through semantic layer
• Experience building and maintaining dbt projects with testing, documentation, and CI/CD pipelines
• Modern, type-hinted, readable. You understand Python-based data pipelines and automation workflows
• You have used an LLM coding assistant (CoCo, Cursor, GitHub Copilot, Claude, or equivalent) as your primary development environment. Daily usage is the baseline
• You can write a semantic view configuration or structured skill file that handles edge cases and encodes enough domain knowledge that the model behaves like a subject matter expert
• You write code, but your output needs to make sense to a business leader who has never opened a terminal. You are the translation layer between what Snowflake's AI can do and what the customer actually needs
• Cortex Analyst, Cortex Agents, Cortex Search, semantic views, Dynamic Tables
• Owns the outcome: Tracks adoption after go-live, identifies stall points, and re-engages until the data product is reliable and can be handed over to run teams
• Codifies, doesn't customize: Instinct is to turn patterns into reusable templates and playbooks that the next engineer can deploy at the next customer, not to build bespoke every time
• Comfortable with ambiguity: Engages with customers to derive requirements, prototypes fast, gathers feedback, and iterates
• Signal clarity: Distills messy deployments into clean, actionable feedback for Leaders, explaining root causes and suggesting fixes, not just reporting problems
• 8+ years of experience in analytics engineering, data engineering, or a related technical role, with at least a portion of it customer-facing or cross-functional
• Daily use of an AI coding assistant as a primary development tool
• Proficient in SQL; can write window functions and complex joins without referencing documentation
• Experience with dbt
• Has shipped production data model or pipeline that non-technical business users actually relied on
• Comfortable in Git (PRs, branches, code review)
• Demonstrable experience translating business requirements into technical specifications
• We do not sponsor H1B visas
• Experience with Airflow or other orchestration frameworks
• Familiarity with enterprise business systems (ERP, CRM, HRIS, or similar)