Data and Analytics Engineer

GoEngineer is hiring a Data & Analytics Engineer to manage and improve the lifecycle of data across business applications, the data warehouse, and business users. The role combines data engineering, analytics, systems troubleshooting, data quality, and stakeholder partnership to deliver reliable data solutions.

Responsibilities:

• Own the full data lifecycle, understanding where data originates, how it changes, and how it moves across business applications and the data warehouse
• Design, build, and maintain data models, transformations, pipelines, and scheduled processes that support reporting, analytics, and AI applications
• Manage data integrations between NetSuite, the warehouse, and other enterprise systems, including data written back to source applications
• Ensure data quality and reliability by monitoring pipelines, warehouse performance, and data flows, and resolving issues at their root cause
• Support warehouse operations, including job scheduling, access management, storage, and performance optimization
• Apply source control, testing, documentation, and controlled deployment practices to data code and processes
• Develop and support the data services and integrations behind AI tools and agents
• Partner with business stakeholders to understand their questions, identify available data and gaps, and translate needs into practical data and reporting solutions
• Look beyond the initial request to determine the underlying business problem and recommend solutions that deliver the right outcome
• Communicate scope, timelines, and trade-offs clearly, using data to guide recommendations and decision-making
• Identify opportunities to improve data processes, systems, and workflows, collaborating with teammates to turn ideas into practical solutions

Requirements:

• 2–4 years of experience in data engineering, analytics engineering, or a closely related role. We're open to candidates slightly outside that range with the right experience
• Strong SQL skills, ideally T-SQL with SQL Server or Azure SQL, including stored procedures, query tuning, and working with large datasets
• Experience with data warehousing and dimensional modeling, including fact and dimension tables, incremental loads, and maintaining historical data through temporal tables or slowly changing dimensions
• Ability to monitor and troubleshoot warehouse health, including failed jobs, data freshness, and performance issues
• Working knowledge of Power BI, including building data models and dashboards
• Experience using Git and pull-request workflows for source control
• Ability to understand and troubleshoot data flows across interconnected business applications and a central data warehouse
• A strong understanding of data quality and how changes in one system can affect downstream data and reporting
• Ability to translate business questions into data requirements, identify gaps, and constructively challenge assumptions when needed
• Clear communication with both technical and business stakeholders, including explaining recommendations, setting expectations, and recognizing when more information is needed
• Self-direction and initiative, with the ability to investigate problems, identify improvements, and collaborate with the team to move solutions forward
• We're also looking for experience in at least 2–3 of the following areas. Comparable tools count; you don't need experience with every tool listed
• dbt or another SQL transformation framework
• NetSuite data, or another ERP or CRM such as Salesforce, Dynamics, or SAP
• Celigo or Stitch, or a similar integration tool such as Fivetran, Azure Data Factory, or Boomi
• Python, TypeScript, or PowerShell
• Azure services such as Functions, Key Vault, and app hosting, or AWS or GCP equivalents
• AI coding assistants or building data access for LLMs