• Design, develop, and maintain data pipelines using Azure Databricks • Build and optimize data transformations using PySpark and SQL in Databricks • Implement and maintain Lakehouse architectures using Delta Lake • Develop ETL/ELT pipelines orchestrated through Azure Data Factory • Integrate data from multiple sources into the data platform and analytical layers • Design and maintain data models and data warehouse structures for analytics • Ensure data quality, scalability, and performance of large-scale data processing pipelines • Collaborate with BI teams to support Power BI and reporting platforms • Support and evolve existing SQL Server data platforms and ETL solutions (SSIS) when required • Contribute to the design of modern cloud-based data architectures

• Tackle fraud daily by designing and deploying innovative security scenarios • Build and maintain robust data pipelines to support regulatory reporting for financial institutions • Collaborate with teams across Fraud, Compliance, Security, and Tech • Write clean, reusable, and well-documented code, conduct thorough testing • Promote best practices in software development

Role Description We’re looking for someone with experience contributing to Wikidata and an understanding of how structured, open data is used across platforms like search engines and knowledge panels. You’ll help ensure that information related to BorrowMyDoggy is accurately represented in publicly available datasets, in line with community guidelines and best practices. This is a research-led, detail-oriented role, focused on data accuracy, sourcing, and structuring information in a neutral and compliant way. We’re a fun go-getter team who are incredibly supportive and always take initiatives to come up with ways to improve BorrowMyDoggy. Responsibilities - Review existing Wikidata entries related to BorrowMyDoggy and associated entities - Add or improve structured data (e.g. company information, key people, attributes) - Ensure all contributions follow Wikidata and Wikimedia guidelines - Source and reference reliable, third-party information - Identify gaps or inconsistencies in publicly available data - Advise on best practices for maintaining accurate open data presence Qualifications - Experience contributing to Wikidata - Strong understanding of reliable sources and citation standards - Attention to detail and structured thinking - Ability to work within neutral, non-promotional guidelines Requirements - Familiarity with Wikipedia policies (especially conflict of interest) - Experience with knowledge graphs / SEO / structured data Benefits - Work with a fun and friendly dog-loving team - Help shape the voice of a mission-driven brand - Create content for a large and engaged dog-loving community

• Lead the data engineering efforts and drive architectural decisions for the Data Platform • Design, build, and optimise scalable Data Warehouses and data workflows using Snowflake • Deploy, monitor, and manage reliable data pipelines in a production environment • Bridge the gap between Data Engineering and DevOps by configuring and maintaining AWS infrastructure • Automate infrastructure and application deployments using Terraform, Docker, and CI/CD tools • Work autonomously while fostering collaboration, mentoring peers, and ensuring best practices