Senior Data and Analytics Engineer

Velir is an established mid-sized agency with a top-tier portfolio of clients, ranging from the world’s largest non-profits to Fortune 500 brands. As of 2023, Velir acquired Brooklyn Data Company, a premier data and analytics consultancy focused on leadership, process improvement, implementation, and advanced analytics. At Velir, we believe people are our greatest asset. Our culture is built on a foundation of trust, collaboration, and continued improvement. We strive for excellence in everything we do, embracing challenges as opportunities for growth. Our success is driven by a shared passion for making a positive impact on our customers, our communities and each other. We are a remote first company that offers competitive pay and excellent benefits. Overview The  Senior Data & Analytics Engineer  will  play a crucial role in helping our client organizations transform raw data into reliable, well-modeled assets that drive business decisions. This hybrid role bridges data engineering and analytics engineering — combining the infrastructure-building strengths of a data engineer with the transformation, modeling, and semantic layer  expertise  of an analytics engineer. Core responsibilities include Data Architecture Design, Data Modeling, ELT/ETL Development, Data Quality Assurance, and Scalability Optimization, with a strong emphasis on Databricks as the primary platform. Because our clients are mostly US-based organizations, we look for the ability to communicate with professional  proficiency  in English, verbally and in writing. This role is open to US based applicants only. Responsibilities   Data & Analytics Engineering Expertise:  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. You will operate across both the data engineering and analytics engineering layers of the data stack, owning pipeline development and maintenance, as well as designing and building the resultant data models.   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   Cross-Team Collaboration:  You are responsible for collaborating with peers and other functional departments to develop and implement data strategies that support engagement goals and client needs.   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   Project Delivery:  You are responsible for ensuring that data engineering and analytics engineering projects are delivered on time, within scope, and within budget.   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   Skills & Qualifications   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   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   Strong analytical and problem-solving skills   Excellent communication and collaboration skills   Familiarity with leveraging coding assistants such as Claude Code, CoPilot, and/or Codex is a plus   Physical Requirements Frequent sitting at a desk performing work on a computer Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions Compensation Range: $160,000 - 175,000  annually Please note that compensation packages are finalized after the interview process is co