Senior Data Engineer

Role: Senior Data Engineer
Location: Minneapolis, MN
Experience: 15+ Years

ROLE OVERVIEW
The Senior Data Engineer is an experienced individual contributor within the Technology & Data organization, responsible for designing, building, and maintaining scalable data infrastructure that supports analytical and operational needs. Working independently with minimal supervision, the Senior Data Engineer leads the technical delivery of complex data engineering work: identifying data sources, ingesting and transforming data, and modeling it for usability across the organization.

This role requires deep technical expertise in modern data stack technologies including dbt, Snowflake, and Fivetran combined with strong data modeling capabilities and a collaborative, problem-solving mindset. The Senior Data Engineer partners across engineering, analytics, and business teams to enable data-driven decision-making and continuously improve data quality, performance, and reliability.

KEY RESPONSIBILITIES
Data Modeling & Architecture
Design and maintain conceptual, logical, and physical data models that support scalable business analytics and reporting.
Build and optimize dimensional models (star and snowflake schemas) for analytical use cases in Snowflake.
Apply data partitioning and indexing strategies to maximize query performance and cost efficiency.
Collaborate with analytics engineers and solution architects to ensure models align with downstream reporting needs.
Document data models, lineage, and definitions in SQLdbm and dbt docs to support team understanding and governance.

dbt Development & Transformation
Develop, test, and maintain dbt models for ingestion, transformation, and presentation layers within the data warehouse.
Write clean, modular, well-tested dbt SQL following team coding standards and best practices.
Implement dbt tests (schema tests, custom tests) to ensure data quality and model reliability.
Manage dbt project structure, sources, seeds, and macros to keep the codebase maintainable and scalable.
Conduct peer code reviews and contribute to engineering standards for dbt development across the team.

Data Pipeline Development & Orchestration
Design and build end-to-end ETL/ELT pipelines that are scalable, maintainable, and resilient.
Configure and manage Fivetran connectors to ingest data from SaaS platforms, databases, and APIs into Snowflake.
Leverage orchestration tools (e.g., Airflow) for scheduling, dependency management, and error handling.
Monitor pipeline performance, implement alerting, and troubleshoot data issues promptly to minimize downtime.
Conduct user acceptance testing (UAT) and manage code releases for reliable system performance.

Snowflake Platform Engineering
Build and optimize data solutions on Snowflake, leveraging native features such as Tasks, Streams, and dynamic tables.
Manage Snowflake objects including databases, schemas, warehouses, roles, and access controls.
Apply cost-optimization strategies including clustering keys, query profiling, and warehouse right-sizing.
Integrate Snowflake with upstream ingestion tools (Fivetran) and downstream BI platforms (Power BI, Tableau).

DevOps & Code Management
Use version control systems (Azure DevOps, GitHub) for collaborative development, branching, and pull requests.
Follow CI/CD practices including automated testing and deployment workflows for data pipeline releases.
Participate actively in agile ceremonies; contribute to sprint goals and continuously improve team delivery processes.

Collaboration & Communication
Collaborate with analysts, analytics engineers, and solution architects to align data solutions with strategic goals.
Translate business requirements into scalable, well-documented technical data solutions.
Communicate effectively across technical and non-technical stakeholders, clearly explaining data concepts and trade-offs.
Drive cross-team collaboration to resolve complex data issues and deliver results.