Senior Data Engineer

University of Colorado•Hybrid, CO

7d•$89,926 - $114,386•Hybrid

About The Position

The Senior Data Engineer independently designs, develops, optimizes, and supports backend data solutions that enable reliable analytics, reporting, and operational use of institutional data. The role is focused on ETL/ELT development, data pipeline orchestration, cloud data infrastructure, relational and dimensional data modeling, data warehousing/lake house solutions, integration frameworks, and modernization of legacy Microsoft data platforms. This position works primarily with technologies such as Azure Data Factory, Azure SQL and SQL Server, Azure storage services, SQL/T-SQL, Python, and related Microsoft data engineering tools. The Senior Data Engineer may interact with data owners, business stakeholders, or clients to clarify requirements and troubleshoot source or integration issues. Power BI or other front-end BI experience is beneficial for understanding downstream consumption, but dashboard design and visualization are not the primary focus of the role.

Requirements

A bachelor’s degree in computer science, information systems, data science, engineering, or a directly related field from an accredited institution.
A combination of education and related technical/paraprofessional experience may be substituted for the bachelor’s degree on a year-for-year basis.
At least two (2) years of progressively responsible experience in data engineering, data integration, database development, business intelligence engineering, data warehousing, or a closely related technical field.
Applicants must meet minimum qualifications at the time of hire.
Applicants must be legally authorized to work in the United States without requiring sponsorship.
Knowledge of modern data engineering, data warehousing, lakehouse architectures, data integration patterns, and enterprise data management principles.
Knowledge of data quality, data governance, metadata management, data lineage, security, privacy, and maintainability best practices.
Skill in analyzing complex technical problems, evaluating alternatives, and applying sound judgment to develop effective and scalable solutions.
Skill in communicating technical concepts, recommendations, risks, and trade-offs effectively to both technical and non-technical stakeholders.
Skill in leading technical design discussions and documenting solution architectures, implementation approaches, and operational considerations.
Ability to manage multiple priorities, meet commitments, and deliver high-quality results in Agile and collaborative team environments.
Ability to establish and maintain effective working relationships, mentor and support team members, and contribute to a culture of continuous learning and improvement.
Ability to balance business needs, technical requirements, security considerations, operational support, and cost factors when developing and recommending solutions.

Nice To Haves

Experience designing, developing, and supporting enterprise data solutions, including ETL/ELT pipelines, data integration frameworks, data warehouses, data marts, lakehouses, and production data workflows.
Experience with Azure and Microsoft data platform technologies such as Azure Data Factory, Azure SQL, Azure Data Lake Storage (ADLS), Blob Storage, Functions, Databricks, Synapse, Microsoft Fabric, Spark, or similar cloud-based data engineering services.
Strong experience with relational databases and SQL development, including complex queries, stored procedures, performance tuning, data modeling, and implementation of scalable analytical data platforms.
Experience developing reusable data engineering capabilities, including frameworks for data quality, observability, metadata management, data lineage, automated testing, CI/CD, and DevOps practices.
Experience modernizing and migrating legacy data and business intelligence platforms (e.g., SQL Server, SSIS, SSAS), and/or supporting data environments within regulated industries such as healthcare, higher education, research, or finance.
Power BI or other front-end BI experience is beneficial for understanding downstream consumption, but dashboard design and visualization are not the primary focus of the role.

Responsibilities

Architect, develop, optimize, and operate complex batch and event-driven pipelines across diverse enterprise sources.
Create reusable SQL, Python, Spark, and orchestration patterns with robust logging, exception handling, and alerting.
Lead modernization of legacy ETL and Microsoft BI integrations into approved Azure or Fabric architectures.
Make solution-level design decisions across Azure Data Factory, Azure SQL, storage, Functions, Databricks, Synapse, Fabric, and related Microsoft data platform components.
Support migration and modernization of legacy data processes and Microsoft BI infrastructure into scalable Azure-based architectures.
Define technical solution architecture for data lakes, lakehouses, warehouses, data marts, integration layers, and foundational data services.
Build solutions with attention to scalability, security, performance, and cost-conscious use of cloud and AI resources.
Maintain relational and dimensional data models, including star schemas, facts, dimensions, semantic structures, and warehouse structures.
Design complex integrations across databases, APIs, files, applications, and streaming or semi-structured sources.
Partner with BI Developers and data owners on grain, history, definitions, lineage, semantic needs, and service expectations.
Design automated validation, reconciliation, observability, lineage, and data-quality frameworks.
Establish source control, code reviews, testing, CI/CD, release, monitoring, and incident-response practices.
Lead root-cause analysis and resolution of complex pipeline, integration, and performance incidents.
Lead peer/design reviews, knowledge sharing, and technical guidance that improves consistency, maintainability, and supportability across the data environment.
Maintain architecture decisions, technical specs, mappings, and support models.