Senior Data and Analytics Engineer

Velir is an established mid-sized agency with a top-tier portfolio of clients, including Fortune 500 brands. The Senior Data & Analytics Engineer will play a crucial role in transforming raw data into reliable assets that drive business decisions, bridging the gap between data engineering and analytics engineering.

Responsibilities:

• You are responsible for building and maintaining the infrastructure and transformation layers that support the storing, movement, and modeling of data — enabling analysts, AI agents, and business stakeholders to confidently consume, interpret and take action
• Design, build, and maintain scalable data pipelines and ELT workflows, with Databricks as the primary platform
• Develop and maintain dbt models that are well-documented, tested, and structured for long-term maintainability
• Own data modeling decisions and methodology — defining entities, relationships, and grain — in collaboration with analytics and business stakeholders
• Consistently seek out and deliver on engagement-level vision, tasks, and problems
• Regularly delivers meaningful improvements to clients' data infrastructure and analytical capabilities
• Autonomous in approach and provides technical guidance to less experienced engineers
• Collaborate with other data engineers and analytics engineers, data analysts, and clients on end-to-end data requirements
• Partner with clients and functional managers to plan for data engineering and modeling needs for product or feature launches
• Create processes and/or documentation templates that help cross-functional teams solve common data problems
• Drive data solution improvements that impact the client experience or empower internal stakeholders to do their jobs effectively
• Take initiative to identify, communicate, and solve important problems, coordinating with others on cross-cutting technical issues
• Architect and design data systems and transformation layers using patterns that allow for iterative delivery and future scaling
• Proactively identify and address technical debt with careful evaluation of development cost
• Optimize for predictability and a regular cadence of deliverables
• Keep reliability, maintainability, and scalability of client systems top of mind
• Ability to identify and prioritize unowned or unglamorous work that enables the broader team to move faster

Requirements:

• Proven experience as a Data Engineer, Analytics Engineer, or in a hybrid or generalist role, with a focus on designing and developing data pipelines and transformation layers
• Strong programming skills in Python and SQL
• Hands-on, deep expertise with Databricks — including Delta Lake, Unity Catalog, and Databricks workflows
• Strong proficiency with dbt for data transformation, testing, and documentation
• Demonstrated experience in data modeling — including dimensional modeling, entity-relationship design, and semantic layer development
• Deep knowledge of data warehousing and ELT/ETL processes
• Familiarity with data integration and orchestration platforms (e.g., Fivetran, Apache Airflow, Azure Data Factory)
• Experience with cloud platforms such as AWS, Azure, or Google Cloud
• Strong analytical and problem-solving skills
• Excellent communication and collaboration skills
• Experience with other cloud data warehouses such as Snowflake or Motherduck is a plus
• Experience with streaming solutions such as Spark Streaming or Kafka is desirable but not required
• Familiarity with MLOps techniques and platforms is a plus but not required
• Familiarity with leveraging coding assistants such as Claude Code, CoPilot, and/or Codex is a plus