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. 

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