Staff Data Engineer

DAT; Denver (CO); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Remote/hybrid flexibility

Parental leave

Wellness allowance

Career development paths

Stock purchase program

401(k) matching

Job summary

DAT is seeking a senior data platform engineer to lead the evolution of a scalable data platform. You will apply advanced patterns like event-driven architecture, CQRS, and data mesh while guiding standards for dbt, Airflow, Snowflake, Kafka, and ETL processes.

You will partner with senior leadership on strategic data projects, architect enterprise-scale infrastructure with strong security, monitoring, and CI/CD practices, and drive data modeling across Kimball, Inmon, or Data Vault approaches.

Qualifications

• 7+ years of relevant engineering experience with large-scale distributed systems.
• Strong data engineering background using dbt, Snowflake, Kafka and cloud services.
• Experience with Agile methods and full technology management lifecycle.

Responsibilities

• Lead design and evolution of a scalable data platform architecture.
• Apply architectural patterns including event-driven, CQRS, event sourcing, and Saga.
• Govern standards for dbt, Airflow, Snowflake, Kafka, and ETL processes.
• Partner with senior leadership on strategic data initiatives and IT alignment with business goals.
• Architect enterprise-scale data platforms with security, monitoring, and CI/CD practices.
• Model data using Kimball, Inmon, or Data Vault concepts and move toward Data Mesh.
• Solve complex business problems with data-driven insights and measurable impact.
• Enforce coding standards, reviews, source control, testing, and governance.

Skills

Data engineering

dbt

Snowflake

Kafka

Airflow

Terraform

Python

SQL

ETL

CI/CD

Education

Bachelor's degree in CS / CE or related

Tools

dbt

Snowflake

Kafka

Airflow

Terraform

Tableau

MySQL

Oracle

AWS

Job description

• Platform Evolution: Lead the design and evolution of our modern data platform architecture, ensuring it remains scalable, available, and performant
• Pattern Implementation: Apply in-depth knowledge of advanced architectural patterns—such as Event Driven, CQRS, event sourcing, and Saga patterns to solve complex data challenges
• Technical Governance: Develop, document, and communicate standard methods and processes to be utilized within our technology stack, including dbt, Airflow, Snowflake, Kafka, and traditional ETL
• Strategic Collaboration: Act as a key partner to senior leadership on strategic data projects and build strong working relationships with other IT leaders by providing technical acumen and thought leadership
• Enterprise-Scale Infrastructure: Architect and manage enterprise-scale data platforms while setting best practices for development teams, including security, privacy, monitoring & alerting, and CI/CD
• Advanced Data Modeling: Implement sophisticated data modeling strategies, including Kimball, Inmon, or Data Vault, and drive the transition toward modern concepts like Data Mesh
• Complex Problem Solving: Provide solutions for complex business problems, enabling insights that can empower better decision-making across the organization
• Operational Excellence: Set and enforce best practices for the full software development and technology management life cycle, including coding standards, code reviews, source control management, and testing
• Culture of Excellence: Take an active role in fostering a culture of learning and excellence within the engineering organization
• Influence through Data: Leverage data to prioritize work and influence others across the organization
• Change Management: Proactively implement change and improvements when seeing opportunities to optimize the platform or processes
• Current Technologies Being Used Today:
• Data Warehouse: Snowflake, Oracle, MySQL
• Transformation: dbt (data build tool)
• Orchestration: Apache Airflow
• Programming: Python, SQL
• Streaming: Kafka
• Cloud: AWS (S3, EMR, Glue)
• Infrastructure: Terraform

Benefits

• Remote/hybrid flexibility
• Parental leave
• Wellness allowance
• Career development paths
• Stock purchase program
• Immediate 401(k) matching
• Mental health support
• FSA/HSA options
• Life insurance
• 20 days PTO from day one, plus 10 paid holidays annually
• Leadership training, mentorship programs, education reimbursement & industry certifications
• Performance bonuses & annual merit increases
• Premium health coverage, plus dental & vision

Education: Bachelor’s degree in Computer Science, Computer Engineering, or a related technical disciplineExperience: 7+ years of relevant professional engineering experience, with a proven track record of building and operating large-scale distributed systemsCommunication: Excellent verbal and written communication skills with an interest in continuous learning and improvementData Engineering: Strong data engineering background working with dbt, Snowflake, Kafka, and cloud-based services (AWS S3, EMR, Glue)Infrastructure & Tooling: Hands-on experience with Terraform, Airflow, Tableau, MySQL, and OracleSoftware Engineering: Deep knowledge of professional software engineering practices for the full technology management life cycle; experience with Agile is preferred