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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** Mgic Investment Corporation - **Location:** Milwaukee, WI, United States - **Experience:** Expert - **Salary:** $105,590.0 - $179,510.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon S3, Data Analysis, ARM Architecture, Automation of Tests, Cloud Computing, Cloud Database, Code Review, Information Systems, Databases, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Data Architecture, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Warehousing, Identity and Access Management, Python (Programming Language), Operational Databases, Query Optimization, Standard Sql, Shell Script, Software Engineering, Macros, Sql Optimization, Snowflake, Git, Information Technology, Data Analytics, Functional Programming - **Published:** August 5, 2026 - **Apply:** https://mgic.wd5.myworkdayjobs.com/MGIC/job/Milwaukee-WI/Senior-Data-Engineer_R2239 ## About the Role * Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or relevant experience. * 5 or more years of data engineering experience, including cloud data warehousing, dimensional data modeling, ETL/ELT, analytics enablement, and production pipeline support. * Hands-on Snowflake experience, including advanced SQL, data loading and transformation, virtual warehouse sizing, query optimization, access controls, and cost-conscious platform operation. * Experience building production-grade dbt projects with modular models, tests, documentation, source freshness, incremental models, macros, packages, and CI/CD. * Experience developing and operating Apache Airflow DAGs; familiarity with Astronomer or another managed Airflow platform, including deployment, monitoring, alerting, and troubleshooting. * Experience implementing and supporting managed ELT with Fivetran, including connector setup, incremental synchronization, schema evolution, monitoring, and issue resolution. * Strong Python and SQL skills; experience with APIs, data formats, shell scripting, and Git-based development workflows. * Experience with AWS services such as S3, Lambda, IAM, and related cloud data services, plus Agile delivery and end-to-end automation practices. * Knowledge of Cortex Code and AI-assisted software development practices, or demonstrated willingness and ability to build a responsible adoption plan that integrates the technology into solution delivery processes while maintaining security, governance, code-review, testing, and change-management standards. * Strong communication, problem-solving, and collaboration skills, with the ability to influence technical decisions and mentor other engineers. ## Description We are looking for a Senior Data Engineer who is passionate about building trusted, scalable data products with modern cloud technologies. As part of the Data & Analytics team, you will design and deliver a Snowflake-centered data platform, automate source-to-warehouse ingestion with Fivetran, develop analytics-ready transformations with dbt, and orchestrate production workflows with Astronomer and Apache Airflow. Responsibilities: * Define and evolve data integration frameworks, engineering standards, reusable patterns, and governance practices for a modern cloud data platform. * Design scalable Snowflake data architectures, including databases, schemas, tables, views, virtual warehouses, role-based access, and approaches for performance and cost optimization. * Build and operate reliable batch and incremental ingestion pipelines using Fivetran connectors, including source configuration, schema-change handling, sync monitoring, troubleshooting, and custom connector patterns when needed. * Develop modular, maintainable dbt models in Snowflake; implement source definitions, tests, documentation, lineage, incremental strategies, and reusable macros. * Author, schedule, deploy, and monitor data workflows with Apache Airflow on Astronomer, applying effective dependency management, retry, alerting, backfill, and failure-recovery practices. * Implement observability and data-quality controls across ingestion, orchestration, and transformation layers so production data is accurate, timely, and available to stakeholders. * Partner with business, analytics, architecture, security, and engineering teams to translate requirements into durable data products and a long-term platform roadmap. * Deliver changes through Git-based development, automated testing, code review, and CI/CD practices across dbt and Airflow projects. * Troubleshoot data and pipeline issues across source systems, Fivetran, Astronomer, dbt, Snowflake, and downstream consumption layers. * Evaluate Cortex Code capabilities and lead the development of a practical adoption plan for incorporating AI-assisted engineering into solution delivery processes, including prioritized use cases, governance and security guardrails, developer workflows, enablement, success measures, and a phased rollout. * Lead design and code reviews, share engineering best practices, and mentor junior data engineers. ## 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) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) - [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) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Modularity: Let's dig deeper](https://www.wearedevelopers.com/videos/1200-modularity-let-s-dig-deeper) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Dev Digest 162: AI careers, MCP, AWS best practices & floppy sweaters](https://www.wearedevelopers.com/magazine/571-dev-digest-162-ai-careers-mcp-aws-best-practices-floppy-sweaters)