We are looking for a Data / Analytics Engineer to join an ongoing initiative focused on building a scalable, AI-driven data platform for product analytics. This is a new role within an existing analytics team, where you’ll help architect and build the core data layer that will enable self-service analytics across the company. The role combines hands-on data engineering with forward-looking AI automation initiatives. Client/Project: is a global DevOps company. The project focuses on building a Gold data layer that enables product and business stakeholders to access ready-to-use, reliable datasets without relying on ad-hoc requests. It’s a complex and high-impact domain, where: * you will design and build a scalable data model from multiple sources (Snowplow, FullStory, internal systems) * the team is transitioning to an AI-first development approach (Cloud Code, Codeium — no manual SQL writing mindset) * AI agents are a core part of the vision (automating data transformations and schema evolution) * the work has a direct impact on decision-making across a ~600-person product organization
Cooperation: full-time, short-term (6 months with the high possibility of prolongation) Stage: existing (early stage of data platform transformation — Gold layer not yet built) Position: new Timezone requirements: CET ±2h preferred Location requirements: Remote Client team: Analytics Guild (up to 9 people) English: Upper-Intermediate+
Requirements: * 4+ years of experience in Data Engineering / Analytics Engineering (Mid+ or Senior level) * Strong SQL skills (advanced: window functions, CTEs, joins, performance tuning) * Hands-on experience with dbt (models, testing, documentation) * Experience with Amazon Redshift (or strong AWS DWH background) * Solid understanding of data modeling (dimensional modeling, medallion architecture) * Experience with Python for data pipelines, integrations, or data quality * Practical experience using AI coding tools (Copilot, Codeium, Claude, etc.) in daily work * Experience working with product/event data * Strong ownership and ability to work autonomously * Clear communication skills and the ability to work with non-technical stakeholders
Nice to have: * Experience building or experimenting with AI agents (LangChain, OpenAI API, etc.) * Familiarity with Snowplow or similar event tracking tools * Familiarity with FullStory or behavioral analytics tools * Experience with AWS ecosystem (S3, IAM, etc.) * Experience with orchestration tools (e.g., Airflow) * Understanding of BI tools (e.g., Looker) and data consumption patterns
Responsibilities: This role evolves in two main phases: Phase 1 (Months 1-4): Building the Gold Layer * Design and implement Gold-layer data models in dbt * Transform raw and Silver-layer data into business-ready datasets * Integrate multiple data sources (Snowplow, FullStory, internal systems) * Collaborate with the analytics team to translate business needs into reusable models * Identify and fix inconsistencies in existing data layers * Establish data quality, monitoring, and observability practices
Phase 2 (Months 4-6+): Automation & AI * Design and build AI agents for automating data workflows * Develop systems that detect new incoming events and suggest schema placement * Automate maintenance of the Gold layer * Explore and implement AI-driven approaches for data transformations
Ownership areas: * Gold data layer architecture * Data quality and reliability * AI-driven automation of data workflows * Collaboration with analytics stakeholders * Long-term scalability of the data platform
Ideal candidate: * Has an AI-first mindset and is excited about automation and agents * Thinks like a data architect, not just a query writer * Comfortable working in a fast-paced, direct communication culture * Self-driven and able to operate with minimal supervision * Able to translate business questions into scalable data models * Interested in building systems, not just solving one-off tasks * Comfortable working in a remote, cross-cultural team
Benefits from 8allocate: * Team & Culture: Team events, offsites, and a culture that keeps people connected. * Learning & Development: Budget for courses, certifications, and conferences. * Wellbeing: Flexible support in line with company policy, with options to support your physical and mental wellbeing (sport, mental health, or medical insurance). * Rest & Recovery: Paid vacation and sick leave.
