Senior Data Engineer

Role: Senior Data EngineerLocation: Hybrid in Oak Park Heights, MN (3 days onsite per week)Duration: 6 months with potential to extend or convertRate: $62-$67, dependent on skills and qualificationsPosition OverviewWe are seeking a highly experienced, hands-on Senior Data Engineer who brings a strong software engineering foundation and an architectural mindset to designing and building scalable, reliable data solutions.This is not simply a pipeline development or maintenance role. We need an engineer who can think through complex technical problems, design reusable solutions from the ground up, and personally implement the code behind them. The ideal candidate understands not just how to build something, but why a particular approach is appropriate, what alternatives exist, and how the design will perform and scale over time.You will work closely with Solution Architects, Product Owners, and distributed engineering teams to establish technical patterns, develop modern data pipelines, and guide the implementation of high-quality data solutions within a Snowflake and Azure environment.Required Qualifications8–10+ years of hands-on data engineering experience, including designing and building complex, enterprise-scale data solutions.Strong Python programming and PySpark/Spark development skills, with a solid understanding of programming fundamentals, reusable functions, modular code, and software engineering best practices.Advanced Snowflake experience, including SQL development, performance optimization, data transformations, and scalable pipeline design.Hands-on experience with dbt for data transformations, modeling, testing, and reusable development patterns.Experience with modern data ingestion tools such as Fivetran, including integration of multiple source systems.Strong experience with Microsoft Azure, including relevant services such as Azure Blob Storage, Azure Functions, and Azure DevOps.Demonstrated experience personally designing and implementing reusable, metadata-driven or configuration-driven data engineering solutions, rather than solely maintaining established frameworks.Strong understanding of incremental processing, CDC, pipeline reliability, data quality, and fault-tolerant engineering principles.Experience with data modeling methodologies, including dimensional modeling and layered data architectures.Ability to independently evaluate technical challenges, explain design decisions, and collaborate with architects and engineering teams.Experience providing technical guidance, conducting code reviews, and supporting distributed development teams.Preferred QualificationsExperience building reusable dbt macros using Jinja or developing similar parameterized transformation frameworks using Python, PySpark, or SQL.Experience with Data Vault 2.0 modeling.Experience implementing automated data governance, metadata management, data lineage, and security controls.Familiarity with infrastructure automation and CI/CD practices.Experience building scalable data foundations that support advanced analytics, machine learning, or generative AI initiatives.Key ResponsibilitiesHands-On Engineering & Solution ArchitectureDesign, develop, and optimize scalable data pipelines and reusable engineering frameworks using Snowflake, Azure, SQL, Python, and PySpark.Translate complex business and technical requirements into practical engineering solutions, making informed decisions about architecture, design patterns, performance, and maintainability.Develop modular, reusable code, functions, and components that can support multiple data sources and use cases without duplicating development efforts.Partner with Solution Architects to define technical approaches while independently owning detailed engineering design and implementation.Evaluate technical alternatives, identify potential limitations, and explain the reasoning behind design decisions.Data Platform Development & AutomationBuild and optimize end-to-end data pipelines using technologies such as Snowflake, dbt, Fivetran, and Azure.Design and implement metadata-driven or configuration-driven ingestion and transformation frameworks that simplify onboarding new data sources.Develop reliable incremental processing, change data capture (CDC), and data transformation solutions.Engineer resilient pipelines with appropriate error handling, retries, monitoring, and recovery mechanisms to support consistent and accurate processing.Design layered data architectures and reusable data models using approaches such as dimensional modeling, star schemas, and Data Vault.Performance, Data Quality & GovernanceOptimize Snowflake queries, transformations, warehouse configurations, and data processing patterns to improve performance and manage costs.Implement automated data quality checks, validation, lineage, and governance controls throughout the data lifecycle.Apply CI/CD and DataOps practices using Azure DevOps and related tools to support reliable deployments and consistent engineering standards.Establish observability and monitoring practices to proactively identify pipeline failures, performance bottlenecks, and data quality issues.Technical Leadership & CollaborationServe as a hands-on technical resource and engineering lead for onshore and offshore development teams.Establish reusable development standards, review code, mentor engineers, and guide implementation of complex solutions.Collaborate with business stakeholders to understand ambiguous data requirements, investigate source systems, and translate needs into actionable technical designs.Communicate technical recommendations, architectural tradeoffs, and implementation approaches clearly to both technical and nontechnical audiences.What Will Make Someone Successful in This RoleThe strongest candidate will be a true hands-on engineer who enjoys solving difficult technical problems and building solutions that other engineers can confidently reuse.We are particularly interested in individuals who can demonstrate how they have personally designed and coded reusable data frameworks, solved complex performance or reliability challenges, and influenced engineering decisions through sound technical reasoning.Success in this role requires more than familiarity with modern data tools. It requires strong engineering fundamentals, independent problem-solving, and the ability to balance hands-on development with technical leadership.