Data and Analytics Engineer

Nimble Gravity

. Architect flexible, performant data models driving LOB teams toward single sources of truth across business domains .

Posted 10/5/2026full-timeRemote • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in building and maintaining data infrastructure using SQL, Python, and dbt, with a focus on data governance, security, and automation. Proven ability to translate business requirements into technical specifications and deliver reliable data models for non-technical users.

Highest-signal resume keywords

SQL ProficiencyData Infrastructure DevelopmentExperience with dbtClient-Facing Communication SkillsKnowledge of Snowflake Cortex

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

SQLPythonDbtData ModelingData QAAutomated TestingData GovernanceSemantic View ConfigurationsIncremental Pipeline PatternsComplex Joins

Soft Skills

Client-Facing CommunicationCross-Functional CollaborationTechnical Workshop Facilitation

Tools & Technologies

SnowflakeCortex AnalystGitAirflowCI/CD PipelinesAI Coding Assistant

Industry Keywords

Analytics EngineeringData EngineeringEnterprise Business SystemsERPCRMHRIS

Tech Stack

Tools & technologies

AirflowERPPythonSQL

About the role

Key responsibilities & impact

Architect flexible, performant data models driving LOB teams toward single sources of truth across 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 models only access authorized data
Build semantic data models exposing LOB data to natural-language queries via Cortex Analyst
Define and validate metrics, dimensions, and relationships for AI agents
Identify and resolve data structure, naming, and coverage gaps that could cause agent failures or incorrect results
Document playbooks, reusable data model templates, and semantic model libraries
Run technical workshops to upskill team members
Author semantic view configurations and YAML/Markdown skill files for non-technical analysts
Own the full data stack from source ingestion to semantic layer, ensuring production reliability and maintainability
Work directly with clients, engineers, and AI specialists to turn emerging technology into measurable business outcomes
Track adoption after go-live, identify stall points, and re-engage until the data product is reliable and handed over to run teams
Translate business requirements into technical specifications and provide actionable feedback to leaders

Requirements

What you’ll need

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
Advanced SQL: CTEs, window functions, incremental pipeline patterns
Experience building data infrastructure involving large-scale relational datasets
Experience building and maintaining dbt projects with testing, documentation, and CI/CD pipelines
Modern, type-hinted, readable Python; understanding of Python-based data pipelines and automation workflows
Daily use of an LLM coding assistant such as CoCo, Cursor, GitHub Copilot, Claude, or equivalent
Ability to write semantic view configurations or structured skill files handling edge cases and domain knowledge
Client-facing communication skills
Knowledge of Snowflake Cortex, including Cortex Analyst, Cortex Agents, Cortex Search, semantic views, and Dynamic Tables
Experience with Airflow or other orchestration frameworks is a strong plus
Familiarity with enterprise business systems such as ERP, CRM, or HRIS is a strong plus
Must be eligible to work without H1B visa sponsorship