Data Engineer

Data Engineer $120000 - $160000 per year | Addison, TX | On-site | Permanent Data Engineer - Hybrid A bit about us: Our 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. Why join us? Growing company Top benefits Great culture Job Details 1. Data Engineering & Snowflake Platform | Primary Focus Design and own the three-layer Snowflake architecture: RAW → STAGING → MART Build and maintain domain MARTs (operations, call center, finance, workforce, marketing) using dbt - grain, ownership, SLA, and data contracts defined per MART Own Snowflake RBAC - Domo read-only on MART, AI scoped to mart.ai_features, row-level policies and masking applied where needed Build and maintain Semantic Views as the authoritative metric layer (booked rate, avg ticket, revenue per tech per day) - single source of truth for all tools Monitor pipeline health, warehouse costs, and query performance; optimize proactively 2. Data Ingestion & ETL/ELT | Core Ownership Own ingestion end-to-end - Fivetran is the primary tool; configure and manage connectors for ServiceTitan, QuickBooks/Sage Intacct, Paycom, CCaaS, and marketing platforms Handle sources Fivetran does not cover with lightweight Python scripts (SFTP, REST APIs, email-delivered files); bias toward replacing custom scripts with managed connectors over time Own the dbt project as an engineering artifact - structure, CI/CD, dev/prod environments, packages, documentation Manage schema drift - catch upstream source changes before they break downstream MARTs; configure Fivetran schema change policies and dbt source assertions Define and maintain pipeline SLAs and alerting - failures surface before business users notice Own data lineage - every MART table traceable to source via dbt lineage graph Manage Fivetran MAR costs - optimize sync frequency without sacrificing freshness 3. AI Engineering | Built on the Data Foundation Implement Cortex Analyst + Semantic Views for NL-to-SQL querying of MARTs - no SQL required for GMs and ops users Build enterprise knowledge search via Cortex Search - SOPs, runbooks, and operational docs indexed within the Snowflake perimeter Deploy Cortex Agents orchestrating across Cortex Search and Cortex Analyst for multi-step business questions Connect external systems (ServiceTitan, M365) via Snowflake MCP Server; use N8N or LangChain for workflows outside Snowflake Build AI feature pipelines in mart.ai_features under the same governance standards as all other MARTs 4. Governance & Enablement Define AI governance framework: acceptable use, risk tiers, PII handling, vendor evaluation Ensure AI pipelines inherit existing Snowflake RBAC and masking - no parallel governance layer Train end users on CoWork and Cortex Analyst; build internal tools for ops, finance, and service teams First 90 Days Audit Domo pipelines and produce a source inventory - ingestion method, refresh schedule, and downstream consumers for every source Stand up Snowflake three-layer architecture, RBAC, and dbt project scaffold with dev/prod environments Configure first Fivetran connectors (ServiceTitan first); ship mart.operations with dbt tests passing and Domo read-only connected Deliver AI governance framework and acceptable use policy Present 12-month roadmap to leadership - ingestion milestones gating MART build gating AI capabilities Our Tech Stack Data 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 sources Data Transformation (required) dbt - models, tests, sources, snapshots, incremental, CI/CD BI / Visualization Domo (read-only MART layer) AI / LLM Claude (Anthropic) - direct API and via Cortex Field Service Platform ServiceTitan Productivity Stack Microsoft 365, SharePoint, Teams What We're Looking For Required - Data Engineering 4+ years owning production data pipelines as a data engineer or analytics engineer Expert SQL; strong data modeling - star schema, SCD Types 1/2, incremental load patterns Hands-on dbt in production - models, tests, sources, snapshots, macros, CI/CD, project ownership Managed connector experience - Fivetran strongly preferred; Airbyte, Stitch, or equivalent considered if connector setup, schema mapping, schema change policies, and MAR/MTU cost management are demonstrated Direct Snowflake experience - schema design, warehouse management, RBAC, query optimization, task scheduling; SnowPro Core preferred Domain MART design - grain, SLA, ownership, and data contracts a small team can maintain Pipeline 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 preferred Python for custom ingestion - SFTP, REST APIs, email-delivered files Data quality as default - dbt tests, freshness monitors, and schema assertions in every pipeline Required - AI Foundation Genuine drive to build AI capabilities; understands clean data is the prerequisite to reliable AI LLM API experience (Anthropic, OpenAI, or equivalent) - prompt engineering, structured outputs, basic agent patterns Familiarity with RAG and vector search; able to implement Cortex Search without prior vector DB experience Python for pipeline scripting and API integration Preferred Snowflake Cortex AI hands-on - Cortex Analyst, Cortex Search, Cortex Agents, or Semantic Views Multi-location or multi-brand data environment ServiceTitan or field service platform experience AI governance framework experience (NIST AI RMF or equivalent) Jobot is an Equal Opportunity Employer. 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