Founding Data Engineer

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Company Overview

A growing technology company is developing AI-powered teammates designed to support contractors and service businesses in the plumbing, HVAC, and electrical industries. Its platform replaces fragmented software and repetitive manual processes with AI-powered solutions that help businesses answer calls, book appointments, follow up with customers, recover missed revenue, and improve customer experiences.

The organization has built its products based on extensive firsthand research with contractors and continues to expand its platform across the home services industry. Its technology integrates with platforms such as ServiceTitan and supports a growing customer base.

The organization is seeking a Founding Data Engineer to build and own the company’s unified data layer. This role will establish the architecture, tooling, pipelines, data models, and source-of-truth systems that power customer-facing dashboards, analytics, cross-customer benchmarks, and future business intelligence capabilities.

The successful candidate will be the team’s first dedicated data hire and will have significant ownership over the data architecture and engineering practices. The role requires strong data modeling skills, production pipeline experience, SQL and Python expertise, and the ability to build reliable systems that are accessible to both people and AI agents.

Key Responsibilities

• Build and operate reliable data pipelines that ingest information from multiple sources into ClickHouse.
• Select appropriate data engineering tools and own the end-to-end data ingestion and transformation workflow.
• Transform raw data into clean, documented, and reusable data models.
• Design and implement entity resolution systems to ensure customers and other business entities are consistently represented across billing, support, call, and other data sources.
• Build a source-of-truth data library containing canonical views and standardized metric definitions.
• Establish consistent data definitions that can be used across dashboards, analytics, and business reporting.
• Structure data models, definitions, and documentation so both employees and AI agents can query data and receive reliable results.
• Develop internal tools that allow team members to ask data questions and confidently use the resulting information.
• Implement freshness checks, data quality tests, monitoring, and alerts to identify pipeline issues before they affect customers.
• Conduct ad-hoc analyses, segment investigations, and deep dives to support business and partner-related questions.
• Work closely with engineering teams to understand application schemas, events, system architecture, and data generation processes.
• Provide data engineering input during system and product changes to ensure data remains usable, stable, and easy to model.
• Establish scalable data engineering practices as the organization’s data infrastructure grows.

Required Qualifications

• 4–8+ years of experience in data engineering or analytics engineering.
• Experience building and operating production data pipelines from end to end.
• Strong SQL skills and solid Python programming experience.
• Hands-on experience with ETL and data pipeline tooling such as Airbyte, Fivetran, Dagster, dbt, or comparable technologies.
• Experience with data orchestration and workflow management.
• Practical experience working with columnar or OLAP data warehouses.
• Experience with ClickHouse is highly desirable.
• Experience with other analytical data platforms such as BigQuery, Snowflake, or Redshift is transferable.
• Strong data modeling experience, including designing tables and data structures used by other teams.
• Ability to establish clear and reliable definitions for business metrics and data.
• Experience implementing data quality, monitoring, and reliability practices.
• Ability to work closely with software engineering teams and understand application schemas, events, and architectures.
• Strong ownership mentality and willingness to take responsibility for production data systems.
• Ability to work effectively in a fast-paced engineering environment.

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