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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - **Company:** Logic20\u002F20 Inc. - **Location:** Seattle, WA, United States (Remote available) - **Experience:** Expert - **Salary:** $173,077.0 - $179,863.0 - **Contract:** Permanent contract - **Skills:** Automation of Tests, Microsoft Azure, Code Review, Continuous Integration, Data Governance, Extract Transform Load (ETL), Data Systems, Data Warehousing, Dimensional Modeling, Github, Apache Hive, Python (Programming Language), Performance Tuning, Role-Based Access Control, Power BI, DataOps, SQL Databases, Unstructured Data, User-Centered Design, Enterprise Data Management, Data Processing, Cloud Platform System, Azure Data Factory, Delivery Pipeline, Apache Spark, Git, Microsoft Fabric, Data Lakes, Pyspark, Deployment Automation, Data Analytics, Bicep, Terraform, Data Pipelines - **Published:** August 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ce4422d0d6479ee6 ## About the Role * 10-15 years of cloud data engineering experience, including hands-on experience with Microsoft Fabric. * 2+ years of experience leading technical delivery teams. * Deep expertise in Microsoft Fabric components including Lakehouse, Warehouse, Data Factory, Notebooks (PySpark/Spark SQL), and Power BI integration. * Advanced proficiency in Python, SQL, Spark/PySpark, data warehousing, dimensional modeling, and modern enterprise data platform architectures. * Experience designing, building, and migrating cloud-based data platforms using Azure Data Factory, Synapse Pipelines, and related Azure technologies. * Hands-on experience with CI/CD, DataOps, Git-based development, deployment automation, and Infrastructure as Code (Azure DevOps, GitHub Actions, Bicep, ARM, or Terraform). * Strong understanding of data governance, security, RBAC, automated testing, and data quality validation frameworks. * Excellent consulting, communication, and client-facing leadership skills with the ability to drive technical decisions, mentor engineers, and partner effectively with architects, project managers, and business stakeholders. Preferred: * An undergraduate degree in technology or business. * Microsoft Certifications (Fabric Data Engineer, Azure Fundamentals, Azure Data Engineer Associate, etc.). ## Description As a Lead Data Engineer joining Logic20/20's AI & Analytics practice, you'll help clients scale their data solutions and make data-driven decisions while driving enterprise-level innovation. You'll work closely with the client to understand their business processes and analytics needs to design and build data pipelines and cloud data solutions. You will have the opportunity to guide your client through best practices in data lake, data processing, and data pipeline design to help them achieve their business goals. You will work closely with your team, including analysts, architects, and technical project managers, to design solutions and work together to deliver a world-class solution. You'll strike a balance of technical skills and business acumen to help clients better understand their core needs and what is possible for a future state. What you'll do: * Lead end-to-end design and development of data pipelines, notebooks, lakehouses, and warehouses in Microsoft Fabric * Orchestrate and implement scalable ETL/ELT pipelines integrating structured and unstructured data from multiple sources * Define and enforce data engineering best practices: data modeling, partitioning, performance tuning, data quality * Own the CI/CD strategy for Fabric artifacts: Azure DevOps integration, deployment pipelines, environment promotion (dev/test/prod), automated test scripts validating deployments * Translate business requirements into technical designs, working closely with architects and analysts * Mentor a team of data engineers, conduct code reviews, enforce coding/security standards * Monitor and optimize pipeline performance, cost, and capacity utilization * Track the Fabric roadmap and evaluate new features for adoption ## Related Videos - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Back(end) to the Future: Embracing the continuous Evolution of Infrastructure and Code](https://www.wearedevelopers.com/videos/440-back-end-to-the-future-embracing-the-continuous-evolution-of-infrastructure-and-code) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [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) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [What Are The Top Skills Required For Azure Developers?](https://www.wearedevelopers.com/magazine/77-what-are-the-top-skills-required-for-azure-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)