Senior Analytics Engineer

About the role

About Us

Perk (formerly TravelPerk) is the intelligent platform for travel and spend management. Built to tackle the time-consuming, manual work that gets in the way of real work, our tools automate everything from travel bookings to expenses, invoice processing, and more. By eliminating this shadow work that wastes hours, erodes morale, and saps innovation, we're on a mission to power real work, with real impact.

We're trusted by more than 12,000 companies worldwide, including Bolt, GoCardless, and Fabletics, and we're tackling the 7 hours of lost productivity per employee each week, a $1.7 trillion problem https://www.perk.com/shadow-work-report.

Founded in 2015, Perk has grown into a global company of more than 1,800 people across 12 offices globally, with headquarters in London and Boston. We combine innovation, control, and simplicity to transform how businesses work and how people feel at work.

At Perk, we're driven by our values, like being an owner, delivering a 7-star experience, and working as one team. We value curiosity, purpose, and mindset, not just knowledge, to unlock the power in your potential. Our talent team brings together leading minds from the travel, fintech, and SaaS industries, representing over 70 countries. If you're excited about having a real impact and shaping how millions of people experience work, we'd love you on the team.

Visit www.perk.com http://www.perk.com to learn more.

Overview

This critical role sits at the intersection of Data Engineering and Data Analytics, focusing on transforming raw data into high-quality, trustworthy, and easily consumable datasets and visualisations for business users. The ideal candidate will possess a strong background in SQL, dbt, data modelling, ETL/ELT principles and experience with modern cloud data platforms.

Core Responsibilities

Develop and Maintain Transformation best practices in ELT Pipelines: Design, develop, and maintain efficient and scalable workflows using tools like dbt to transform raw data loaded into our data warehouse (Snowflake) into clean, ready-to-use data models.

Data Modelling: Implement best-practice data modelling and software engineering techniques to ensure data structures are optimised for performance, accuracy, and ease of use in reporting and analytical applications (e.g. CI/CD, testing, lineage).

Data Quality and Testing: Write comprehensive data quality checks, tests, and monitoring scripts to ensure the accuracy, completeness, and reliability of all transformed data assets. Establish and maintain documentation for all data transformations and models.

Collaboration: Work closely with Data Analysts and business users to understand their reporting needs and optimise data models to support their analytical use cases. Collaborate with Data Engineers on data ingestion strategies and platform optimisation.

Performance Optimisation: Tune and optimise SQL queries and data models to reduce latency and improve the performance of our data warehouse and downstream applications.