Junior Certification Engineer Structures & Stress (m/w/d) 80% bis 100%

Keyloop bridges the gap between dealers, manufacturers, technology suppliers and car buyers. We empower car dealers and manufacturers to fully embrace digital transformation. How? By creating innovative technology that makes selling cars better for our customers, and buying and owning cars better for theirs.   We use cutting-edge technology to link our clients’ systems, departments and sites. We provide an open technology platform that’s shaping the industry for the future. We use data to help clients become more efficient, increase profitability and give more customers an amazing experience. Want to be part of it?     Role Overview We are looking for a Snowflake Developer / Data Engineer (IC3) to take self-directed ownership of features and small epics within our Snowflake data platform — including Bronze/Silver/Gold data layer architecture, ELT pipelines, and the data models that power analytics and reporting across Keyloop. Beyond hands-on delivery, you will lead design discussions for your own work, coach more junior developers, and work with stakeholders across Finance, Sales, and Operations to deliver reliable, scalable, and well-governed data products. Key Responsibilities Lead complete delivery of features and small epics on the Snowflake platform, from design through to production, without senior oversight Design and implement Snowflake database objects: tables, views, streams, tasks, stored procedures, and dynamic tables Build and maintain ELT pipelines using Boomi, dbt, or SQL-based transformations into Snowflake Architect and manage Bronze / Silver / Gold data layers in line with Keyloop's data platform standards Develop and optimise complex SQL for data transformation, aggregation, and reporting use cases Implement Snowflake performance best practices: clustering keys, result caching, query profiling, and warehouse right-sizing Own testing strategy for pipeline and model changes, ensuring comprehensive coverage beyond unit-level checks Coach and support IC1/IC2 developers on Snowflake, SQL, and data engineering best practices Lead technical design discussions for your features and represent the team in cross-functional planning meetings Coordinate cross-functional data requirements with adjacent teams (Finance, Sales, Operations) Support data quality checks, reconciliation processes, and lineage documentation Manage Snowflake environment configuration across dev, QA, and production Contribute to data governance: access control, data masking, row-level security, and tagging Monitor pipeline health, proactively identify failures, and provide production support — including occasional out-of-hours incident response Support UAT cycles with technical recommendations and sign-off Produce clear technical documentation: data dictionaries, ERDs, pipeline runbooks Required Skills & Experience 4–7 years of Snowflake development experience in a production data environment Strong SQL skills — window functions, CTEs, recursive queries, and advanced aggregations Hands-on experience with Snowflake-specific features: streams, tasks, dynamic tables, time travel, zero-copy cloning Experience building ELT/ETL pipelines from source systems such as Salesforce, NetSuite, or ERP platforms Understanding of dimensional modelling and data warehousing concepts (star schema, slowly changing dimensions) Familiarity with Snowflake access controls: roles, grants, and row/column-level security Experience with version control (Git) and CI/CD for data pipeline deployment Ability to translate business data requirements into technical data models Demonstrated ability to coach or mentor junior developers, and to lead technical discussions within a team Desirable Skills SnowPro Core or Advanced Data Engineer certification Experience with dbt (data build tool) for transformation orchestration Exposure to Boomi as an integration tool feeding Snowflake Familiarity with Salesforce data structures and Salesforce reporting requirements Experience with NetSuite financial data extraction and modelling Knowledge of data observability tooling (Monte Carlo, Great Expectations, or similar) Experience working within Agile delivery teams and Jira-based project management Background in automotive, SaaS, or financial systems data

WHO WE ARE * Artefact is a leading global consulting firm dedicated to accelerating the adoption of data and AI. We work with a variety of businesses, from supermarket chains, to private equity firms and telecoms; including Nissan, L'Oréal, Carrefour, WHSmith, Orange, Beiersdorf, BNP Paribas, and Samsung. * Our success stems from combining advanced data technologies, agile methods for quick delivery, and dedicated teams of data scientists, data engineers, business consultants, and data analysts. * Our 1,700 employees operate in 25 countries (Americas, Europe, Asia, Middle East, India, Africa) and we partner with 1,000+ clients. WHAT YOU WILL BE DOING As a Junior Software Engineer in our London office, your role will encompass: * working on engineering projects together with senior team members * training and certification to enhance your skills in software engineering, data science, and data architecture. QUALIFICATIONS NECESSARY EDUCATION AND EXPERIENCE * Education: A Bachelor's or Master’s degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative field. * Programming Proficiency: Strong programming skills in Python and a solid understanding of data structures and algorithms. * Software Engineering Fundamentals: A demonstrable understanding of version control and repository tools (Git, CI/CD) and a commitment to writing clean, well-documented code. * Data Skills: Experience with SQL for querying relational databases. * Problem-Solving Skills: An analytical mindset with the ability to break down complex problems into manageable steps. * Eagerness to Learn: A genuine passion for technology and a proactive attitude towards self-development and learning new skills, as demonstrated through projects or coursework. * Communication & Collaboration: Strong verbal and written communication skills, with the ability to work effectively in a team environment and learn from senior members. DESIRABLE EXPERIENCE * Cloud & MLOps Exposure: Basic familiarity with a cloud platform (AWS, GCP, or Azure) and an awareness of concepts like containerization (Docker). * Core ML Knowledge: A good grasp of fundamental machine learning concepts, including supervised/unsupervised learning, model evaluation techniques, and feature engineering. * Advanced Education: A Master's degree or PhD in a relevant field is a strong plus. Hybrid working pattern