Senior Data Engineer
Hybrid: Tuesday/Wednesday in office each week in Denver, CO
*Only those authorized to work in the United States without sponsorship now or in the future will be considered - No third parties or C2C*Job Summary
We are seeking a Senior Data Engineer to help design and build modern, enterprise-scale data platforms and pipelines from the ground up. This is a unique opportunity to join a major data transformation initiative where you will have a significant role in shaping the architecture, engineering standards, and technology patterns of new data platforms.
The ideal candidate has strong experience building greenfield data platforms, designing scalable data pipelines, and working across cloud data technologies. You should be highly proficient in SQL, comfortable troubleshooting complex data problems, and able to translate business and technical requirements into reliable, scalable solutions.
This role is well suited for a senior-level engineer who enjoys solving complex problems and wants to have a direct impact on the design and development of modern data infrastructure.
Key Responsibilities
Data Engineering & Architecture
- Design, develop, and maintain scalable data platforms supporting analytics, business intelligence, reporting, and advanced data workloads.
- Build enterprise data warehouses, data lakes, and Lakehouse architectures.
- Design and implement robust ETL/ELT pipelines that ingest, transform, and integrate data from multiple sources.
- Develop reusable data pipelines, components, frameworks, and engineering patterns.
- Design solutions for both batch and real-time data processing.
- Develop data models and optimize data structures for performance, scalability, and usability.
- Troubleshoot complex data and pipeline issues and develop effective, sustainable solutions.
- Create technical diagrams and communicate proposed solutions to technical and business stakeholders.
Cloud & Data Platforms
- Design and implement cloud-native data solutions using Azure, AWS, and/or Google Cloud Platform.
- Work with modern data technologies such as Databricks, Snowflake, Azure Fabric, Synapse, Azure Data Factory, AWS Glue, Redshift, BigQuery, or similar platforms.
- Implement data orchestration, workflow automation, monitoring, logging, and data lineage capabilities.
- Design for platform reliability, availability, scalability, resiliency, and disaster recovery.
- Apply infrastructure-as-code, source control, CI/CD, and DevOps practices to data engineering solutions.
Data Quality & Governance
- Build data quality, validation, and monitoring processes into data pipelines.
- Establish standards and best practices for data engineering and platform development.
- Implement metadata management, data lineage, cataloging, and data governance capabilities.
- Support appropriate security, access controls, privacy, and compliance requirements.
Collaboration & Technical Leadership
- Partner with data architects, software engineers, analysts, data scientists, and business stakeholders to define data requirements.
- Translate business requirements into scalable technical solutions.
- Participate in architecture and design reviews.
- Provide technical guidance and mentorship to other data engineers.
- Contribute to the strategic roadmap and continued evolution of enterprise data platforms.
Required Qualifications
- 7+ years of professional data engineering experience.
- Demonstrated experience designing and implementing enterprise-scale data platforms, including significant experience building platforms or major data solutions from the ground up.
- Strong, expert-level SQL skills, including query optimization, performance tuning, complex queries, and data modeling.
- Strong experience developing and maintaining large-scale ETL/ELT pipelines.
- Experience with SSIS is highly valued.
- 5+ years of experience working with one or more major cloud/data platforms such as Azure, AWS, GCP, Snowflake, or Databricks.
- Strong programming experience with Python, Scala, Java, or a similar language.
- Experience with distributed data processing technologies such as Apache Spark.
- Strong understanding of data warehousing, dimensional modeling, data lakes, and/or Lakehouse architectures.
- Experience with source control, CI/CD, and DevOps practices.
- Strong problem-solving skills and the ability to work through complex technical challenges independently.
- Excellent communication skills and the ability to explain technical concepts to both technical and non-technical audiences.
Preferred Qualifications
- Experience building and configuring greenfield enterprise data platforms.
- Experience with Microsoft SQL Server and SQL Server Integration Services (SSIS).
- Experience with SQL Server Reporting Services (SSRS).
- Experience with Azure Data Factory, Azure Synapse, Microsoft Fabric, AWS Glue, Redshift, BigQuery, Snowflake, or Databricks.
- Experience with streaming technologies such as Kafka, Azure Event Hubs, or Amazon Kinesis.
- Experience with data governance, cataloging, metadata management, and lineage tools.
- Familiarity with DataOps and/or MLOps practices.
- Experience supporting analytics, AI, or machine learning workloads.
- Cloud, data engineering, or related professional certifications.
- Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field, or equivalent professional experience.
Technical Skills
Data Engineering
- SQL — Expert
- Python / Spark — Advanced
- ETL / ELT — Advanced
- Data Warehousing & Data Modeling — Advanced
- Data Lakes / Lakehouse Architecture — Advanced
Cloud & Data Platforms
- Azure / Microsoft Fabric / Azure Data Factory / Synapse
- AWS
- Google Cloud Platform
- Databricks
- Snowflake
Microsoft Data Technologies
DevOps & Automation
- Git / GitHub
- Azure DevOps
- CI/CD
- Infrastructure as Code
Data Governance & Security
- Data Quality
- Metadata Management
- Data Lineage
- Data Cataloging
- Data Security & Privacy
- Role-Based Access Control
What You'll Bring
- A strong background in data platform engineering, not just pipeline development.
- Experience taking complex data environments from concept and architecture through implementation.
- A passion for building scalable, maintainable data solutions from the ground up.
- Strong analytical and troubleshooting abilities.
- The ability to think architecturally while remaining hands-on technically.
- Excellent communication and collaboration skills.
- A continuous-improvement mindset and interest in modern data engineering practices.
Why This Opportunity?
This is an opportunity to be part of a significant modernization initiative and help build new enterprise data platforms from the ground up. Rather than simply maintaining an existing environment, you'll have the opportunity to influence architecture, establish engineering standards, select and implement modern technologies, and build the foundation for future data and analytics capabilities.