Data Engineer, Analytics

AI Engineer - HybridOur client is a collection of industry-leading residential and commercial HVAC, electrical, and plumbing companies. Our mission is to provide exceptional residential and commercial services by upholding the legacies of our brand partners, empowering their growth, elevating performance, and enhancing the quality of life in the communities we serve.Growing company Top benefits Great culture1. Data Engineering & Snowflake Platform | Primary FocusDesign and own the three-layer Snowflake architecture: RAW ? STAGING ? MARTBuild and maintain domain MARTs (operations, call center, finance, workforce, marketing) using dbt — grain, ownership, SLA, and data contracts defined per MARTOwn Snowflake RBAC — Domo read-only on MART, AI scoped to mart.ai_features, row-level policies and masking applied where neededBuild and maintain Semantic Views as the authoritative metric layer (booked rate, avg ticket, revenue per tech per day) — single source of truth for all toolsMonitor pipeline health, warehouse costs, and query performance; optimize proactively2. Data Ingestion & ETL/ELT | Core OwnershipOwn ingestion end-to-end — Fivetran is the primary tool; configure and manage connectors for ServiceTitan, QuickBooks/Sage Intacct, Paycom, CCaaS, and marketing platformsHandle sources Fivetran does not cover with lightweight Python scripts (SFTP, REST APIs, email-delivered files); bias toward replacing custom scripts with managed connectors over timeOwn the dbt project as an engineering artifact — structure, CI/CD, dev/prod environments, packages, documentationManage schema drift — catch upstream source changes before they break downstream MARTs; configure Fivetran schema change policies and dbt source assertionsDefine and maintain pipeline SLAs and alerting — failures surface before business users noticeOwn data lineage — every MART table traceable to source via dbt lineage graphManage Fivetran MAR costs — optimize sync frequency without sacrificing freshness3. AI Engineering | Built on the Data FoundationImplement Cortex Analyst + Semantic Views for NL-to-SQL querying of MARTs — no SQL required for GMs and ops usersBuild enterprise knowledge search via Cortex Search — SOPs, runbooks, and operational docs indexed within the Snowflake perimeterDeploy Cortex Agents orchestrating across Cortex Search and Cortex Analyst for multi-step business questionsConnect external systems (ServiceTitan, M365) via Snowflake MCP Server; use N8N or LangChain for workflows outside SnowflakeBuild AI feature pipelines in mart.ai_features under the same governance standards as all other MARTs4. Governance & EnablementDefine AI governance framework: acceptable use, risk tiers, PII handling, vendor evaluationEnsure AI pipelines inherit existing Snowflake RBAC and masking — no parallel governance layerTrain end users on CoWork and Cortex Analyst; build internal tools for ops, finance, and service teamsFirst 90 DaysAudit Domo pipelines and produce a source inventory — ingestion method, refresh schedule, and downstream consumers for every sourceStand up Snowflake three-layer architecture, RBAC, and dbt project scaffold with dev/prod environmentsConfigure first Fivetran connectors (ServiceTitan first); ship mart.operations with dbt tests passing and Domo read-only connectedDeliver AI governance framework and acceptable use policyPresent 12-month roadmap to leadership — ingestion milestones gating MART build gating AI capabilitiesOur Tech StackData Warehouse (required) Snowflake + Cortex AI (Cortex Agents, Cortex Search, Cortex Analyst, Semantic Views)Data Ingestion (required) Fivetran (strongly preferred); Airbyte or equivalent considered; Domo Writeback (transitional); Python for uncovered sourcesData Transformation (required) dbt — models, tests, sources, snapshots, incremental, CI/CDBI / Visualization Domo (read-only MART layer)AI / LLM Claude (Anthropic) — direct API and via CortexField Service Platform ServiceTitanProductivity Stack Microsoft 365, SharePoint, TeamsWhat We're Looking For Required — Data Engineering4+ years owning production data pipelines as a data engineer or analytics engineerExpert SQL; strong data modeling — star schema, SCD Types 1/2, incremental load patternsHands-on dbt in production — models, tests, sources, snapshots, macros, CI/CD, project ownershipManaged connector experience — Fivetran strongly preferred; Airbyte, Stitch, or equivalent considered if connector setup, schema mapping, schema change policies, and MAR/MTU cost management are demonstratedDirect Snowflake experience — schema design, warehouse management, RBAC, query optimization, task scheduling; SnowPro Core preferredDomain MART design — grain, SLA, ownership, and data contracts a small team can maintainPipeline monitoring and observability — SLA definition, drift alerting, and incident response before business users are impacted; experience with Elementary, Monte Carlo, or dbt's built-in freshness and source monitors preferredPython for custom ingestion — SFTP, REST APIs, email-delivered filesData quality as default — dbt tests, freshness monitors, and schema assertions in every pipelineRequired — AI FoundationGenuine drive to build AI capabilities; understands clean data is the prerequisite to reliable AILLM API experience (Anthropic, OpenAI, or equivalent) — prompt engineering, structured outputs, basic agent patternsFamiliarity with RAG and vector search; able to implement Cortex Search without prior vector DB experiencePython for pipeline scripting and API integrationPreferredSnowflake Cortex AI hands-on — Cortex Analyst, Cortex Search, Cortex Agents, or Semantic ViewsMulti-location or multi-brand data environmentServiceTitan or field service platform experienceAI governance framework experience (NIST AI RMF or equivalent)Jobot is an Equal Opportunity Employer. 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