Data Engineer

Jobot; Addison (TX); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Top benefits

Great culture

Job summary

Jobot is seeking a Data Engineer to design and own a three-layer Snowflake data architecture in Addison, TX. The role focuses on building MARTs, managing RBAC, and embedding AI-enabled data workflows.

You'll handle ingestion, pipeline quality, and governance while optimizing costs and performance. Ideal candidates bring 4+ years of experience building production data pipelines, expertise in SQL and data modeling, and hands-on dbt, Snowflake, and Fivetran.

Qualifications

• 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

Responsibilities

• Design and own the three-layer Snowflake architecture: RAW → STAGING → MART
• Build and maintain domain MARTs 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)
• Monitor pipeline health, warehouse costs, and query performance; optimize proactively
• Own ingestion end-to-end - Fivetran connectors for ServiceTitan, QuickBooks/Sage Intacct, Paycom, CCaaS, and marketing platforms
• Handle sources Fivetran does not cover with lightweight Python scripts; 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
• 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
• Implement Cortex Analyst + Semantic Views for NL-to-SQL querying of MARTs
• Build enterprise knowledge search via Cortex Search - SOPs, runbooks, and operational docs
• Deploy Cortex Agents orchestrating across Cortex Search and Cortex Analyst
• Connect external systems via Snowflake MCP Server; use N8N or LangChain for workflows
• Build AI feature pipelines in mart.ai_features under governance standards

Skills

SQL

Data modeling

dbt

Fivetran

Python

Snowflake

Tools

Snowflake

dbt

Fivetran

Python

Job description

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. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot’s policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions.

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