Senior Analytics Engineer

About the role

You will define and own the core data models that underpin Relay's Last Mile marketplace, ensuring they serve both analytics and AI workloads with precision and reliability. You will partner closely with engineers and business leaders to translate complex operational requirements into scalable data structures that power critical agentic workflows. You will build the foundational datasets and pipelines that act as the fuel for our most important automated decision systems. You will drive rigorous data quality practices by implementing testing, monitoring, and alerting to maintain trust in every insight. You will collaborate across teams to uncover requirements and guide stakeholders toward optimal data solutions using clear, evidence-based reasoning. You will help shape and evolve our analytics engineering standards and processes as the business scales rapidly. You will leverage AI tools thoughtfully to accelerate your own workflow and amplify the impact of your work.

Key facts

What you'll do

• Design and maintain foundational data models within BigQuery to support analytics, data science, and AI use cases across the Last Mile squad.
• Partner with software engineers to define schema and data contracts that ensure pipelines are robust, performant, and aligned with product needs.
• Build and iterate on datasets that serve as the primary inputs for key agentic workflows powering our marketplace.
• Implement comprehensive data quality frameworks including tests, monitoring, and alerting to detect issues before they impact decisions.
• Collaborate with operations and commercial teams to translate business questions into measurable data definitions and analytical solutions.
• Lead the evolution of analytics engineering best practices, including documentation, modularity, and maintainability of dbt DAGs.
• Develop scripts and automation in Python to transform, validate, and enrich data that cannot be handled efficiently in SQL alone.
• Work with data visualization tools such as Tableau or Looker to surface insights that validate model behavior and inform strategic choices.
• Mentor junior analysts and engineers on effective data modeling, SQL performance, and debugging techniques within the Last Mile environment.
• Champion a culture of experimentation by enabling rapid hypothesis testing through reliable, well-documented data pipelines.

Requirements

• Bring a minimum of 5 years of hands-on experience as an analytics engineer, data engineer, or data analyst in a demanding operational setting.
• Demonstrate deep proficiency in SQL, with advanced capabilities in both BigQuery and PostgreSQL for complex transformations and performance tuning.
• Show a proven track record of designing, building, and maintaining dbt DAGs that are efficient, scalable, and reliable at high data volumes.
• Write clean, maintainable Python for data manipulation, scripting, and automation to support data workflows that extend beyond SQL.
• Exhibit strong experience with data visualization platforms, articulating insights to both technical and non-technical audiences.
• Communicate effectively with stakeholders, gathering requirements and guiding them toward data-backed solutions without sacrificing clarity or rigor.
• Comfortably navigate ambiguous problems, defining scope and success criteria in collaboration with product and engineering partners.
• Commit to continuous learning, applying feedback to refine models, pipelines, and practices in a fast-moving logistics environment.

Nice to have

• Hands-on experience with orchestration frameworks such as Dagster or Airflow to manage complex pipeline dependencies.
• Background building real-time or near-real-time data pipelines that support time-critical decision making.
• Exposure to machine learning workflows, feature engineering, and model evaluation in production-like settings.
• Experience working within teams that own customer-facing products end-to-end, from data to interface.
• Prior success in learning and modelling unfamiliar operational domains in logistics or similar sectors.
• A demonstrated passion for seeing the tangible impact of analytical work on real-world outcomes and customer experience.

Practical notes

The role is located in London with a hybrid working model. The position is full-time. The office is based in Shoreditch, with an expectation of 4 days on-site and 1 day remote to enable in-person collaboration.