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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Associate Data Engineer - **Company:** INSIGHT SOFTWARE LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $72,000.0 - $90,000.0 - **Contract:** Permanent contract - **Skills:** Sql Data Warehouse, Artificial Intelligence, BigQuery, Software Quality, Customer Data Management, Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Systems, Database Queries, Performance Tuning, Query Optimization, Role-Based Access Control, Power BI, SQL Databases, Workflow Management Systems, Enterprise Data Management, Large Language Models, Snowflake, Prompt Engineering, Data Lineage, Virtual Agents, Data Pipelines, Amazon Redshift - **Published:** May 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4f17a3a493c5baa0 ## About the Role Do you have experience in Stakeholder engagement?, * 3+ years of hands-on data engineering or analytics engineering experience in a professional setting. * Strong SQL skills: complex joins, window functions, query optimization, and data modeling. * Demonstrated Power BI proficiency , including report development, data modeling, and DAX. * Experience working with cloud data warehouse platforms (Snowflake, BigQuery , Redshift, or similar). * Proven, active use of AI tools in daily data work, not just awareness. * Ability to engage with business stakeholders and translate requirements into data solutions. * Strong written and verbal communication skills . Preferred * Experience with ELT/ETL orchestration tools such as Fivetran , dbt , or similar. * Familiarity with Salesforce data structures or CRM-adjacent reporting. * Exposure to agentic AI workflows, prompt engineering, or LLM-powered automation. * Background in financial or ARR/revenue data domains. * Experience with role-based access control and data security practices in cloud platforms. ## Description insightsoftware is looking for a data professional who brings strong technical fundamentals, a genuine curiosity for AI, and the ability to translate business needs into data solutions. As a Data Engineer on the Enterprise Data team, you will design and maintain scalable data pipelines, build Power BI solutions that drive decisions, and champion AI-assisted workflows that make the team faster and smarter. This role sits at the intersection of engineering precision and business partnership . What You Will Do Data Engineering * Design, build, and maintain ELT/ETL pipelines that move data reliably from source systems into a cloud data warehouse environment. * Write clean, performant, and well-documented SQL to transform raw data into analyst-ready models and reporting layers. * Partner with the team to define and enforce data modeling standards, naming conventions, and documentation practices. * Monitor pipeline health, troubleshoot failures, and implement proactive alerting to protect data availability. Business Intelligence & Reporting * Develop and maintain Power BI dashboards and reports that give business stakeholders clear, trusted views of company performance. * Apply best practices in data modeling within Power BI (star schemas, DAX measures, performance optimization) to ensure reports are fast and maintainable. * Work closely with business partners to understand reporting requirements, translating ambiguous asks into structured analytical deliverables. * Champion self-serve analytics by building reusable semantic layers that reduce ad hoc request volume. AI-First Ways of Working * Actively use AI tools (such as LLM assistants, copilots, and agentic workflows) to accelerate development, improve code quality, and generate documentation. * Identify opportunities to apply AI and automation to repetitive data tasks, from data quality checks to pipeline scaffolding. * Stay current on emerging AI/data tooling and share practical learnings with the team. * Apply a critical, evidence-based lens to AI outputs, validating results before using them in production. Business Partnership * Engage directly with business stakeholders to understand data needs in context, asking smart questions and pushing back constructively when requirements are unclear. * Communicate technical concepts and data limitations in plain language, building trust with non-technical partners. * Contribute to data governance practices, including metadata documentation, lineage tracking, and data quality standards. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [REST, GraphQL, gRPC, and more: A comparison of modern API styles](https://www.wearedevelopers.com/videos/100247-rest-graphql-grpc-and-more-a-comparison-of-modern-api-styles) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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