Fieldwire

Fieldwire

Data Engineer

United States (Remote)

Sponsorship not specified$120k-$150kDetected 6 days ago
PythonData StructuresSQLBigQuerySnowflakeRedshiftDatabricksAirflowdbtData EngineeringData VisualizationSalesforceCRMERPCommunicationProblem Solving

About the role

  • You will be the owner of our data infrastructure and pipelines - the foundational layer that powers all reporting, analytics, and business decision-making across Fieldwire.
  • You will work closely with Data Insights Managers, Finance, Revenue Operations, and Product to ensure our data lake is reliable, scalable, and production-ready.
  • This is a fully remote role based in the United States.

Responsibilities

  • Design, build, and maintain automated data pipelines that move data from source systems (Salesforce, Xero, Ramp, product databases) into our central data lake and warehouses
  • Own the end-to-end data architecture, including storage strategy, processing systems, and pipeline orchestration
  • Implement and maintain ETL/ELT workflows that extract, transform, and load data into clean, analytics-ready formats
  • Partner with Data Insights Managers and business stakeholders to translate reporting requirements into robust technical data solutions
  • Build automated validation and quality-check layers into every pipeline to prevent bad data from reaching reporting layers
  • Support integration and maintenance of key tools including Salesforce, Xero, Ramp and Greenhouse into the data lake
  • Maintain auditability of all data flows and support compliance and governance requirements
  • Collaborate with the DIM TL and Director of Operations on the data roadmap and architectural decisions
  • The field-first construction platform for less busywork and more building.
  • Designed for easy adoption, crews gain real-time visibility into progress, clear ownership of work, and the context to make confident decisions.

Requirements

  • 2-3+ years of experience in a Data Engineering or equivalent role
  • Experience with cloud data platforms (e.g. Snowflake, BigQuery, Redshift, Databricks)
  • Familiarity with pipeline orchestration tools (e.g. Airflow, dbt, Fivetran, Airbyte)
  • Strong understanding of data warehousing concepts, dimensional modelling, and data lake architecture
  • Experience integrating SaaS platforms (CRM, ERP, finance systems) via APIs and connectors
  • Experience at a SaaS or technology company
  • Familiarity with Salesforce data structures and reporting layers
  • Experience with real-time or streaming data pipelines
  • Knowledge of data governance frameworks and access control models
  • And if you have any of the following, we REALLY want you to apply today!
  • Hands-on experience building and maintaining ETL/ELT pipelines at scale
  • Excellent problem-solving skills with a rigorous approach to data quality and reliability
  • Ability to work independently in a fully remote, fast-moving environment
  • Strong written communication skills - able to document technical decisions clearly for non-technical stakeholders
  • Exposure to BI tools (Power BI, Looker, Tableau) and understanding of how downstream consumers use data

Nice to have

  • Strong proficiency in SQL and at least one scripting language (Python preferred)

Compensation

  • The estimated pay ranges for this role are as follows: $120,000 - $150,000
  • The salary range represents the low and high end of the salary range for this job in the US.
  • The actual salary offer will carefully consider a wide range of factors such as your skills, qualifications and experience.
  • In addition to the salary you may be eligible for a corporate bonus which can range up to 30%.

Benefits

  • Monitor pipeline health in real time; triage and resolve failures quickly to meet data availability SLAs

Company info

  • At Fieldwire, we're looking for our next Data Engineer to have the following skills and experiences

Equal opportunity

  • Fieldwire is proud to be an Equal Opportunity Employer.

This listing is sourced directly from Fieldwire's careers page and normalized into a canonical job model.