Senior Data Engineer

Are you passionate about AI? 🤖 At Satori Analytics, we aim to change the world one algorithm at a time by bringing clarity to global brands through Data & AI. From cloud-based ecosystems for fintech to predictive models for airlines, our cutting-edge solutions cover the entire data lifecycle—from ingestion to AI applications. As a fast-growing scale-up, our team of 100+ tech specialists—including Data Engineers, Data Scientists, and more—delivers innovative analytics solutions across industries like FMCG, retail, manufacturing and FSI. Join us as we lead the data revolution in South-Eastern Europe and beyond! On behalf of a partnering company, that builds enterprise-grade AI systems that operate on terabyte-scale multimodal datasets to power the next generation of marketing intelligence, we're looking for a Senior Data Engineer. The Role Our data engineering practice operates at real scale, across real complexity. The work spans: Large-scale data processing — designing and operating ETL/ELT pipelines with Apache Spark that ingest, transform, and serve terabyte-scale multimodal datasets across the marketing intelligence stack. Polyglot data infrastructure — architecting across relational databases for transactional workloads, vector databases for semantic search and RAG systems, and graph databases for audience relationship and attribution modelling. Data transformation and modelling — orchestrating analytics engineering workflows with dbt to produce well-documented, tested, version-controlled data models that power downstream AI and BI systems. Data platform reliability — building the foundations (orchestration, monitoring, data quality checks, lineage tracking) that keep pipelines and databases dependable under production load. As a Senior Data Engineer, you own the technical arc of the systems in your domain: scoping the problem, choosing the architecture, shipping to production, and operating it under live traffic. You are not writing a design doc for someone else to build — you build it, you run it, and you set the bar for how it gets done. You'll work closely with data scientists and software engineers across the stack, and your decisions will shape how the broader team works. What Your Day Might Look Like: Architect and build production data pipelines and data platforms that serve models, data, and AI workflows to internal and client-facing applications — and stay accountable for them under live conditions. Own non-functional quality across your domain: latency and throughput budgets, scalability, reliability, observability, and cost. Lead the design and operation of multi-model data stores — relational (PostgreSQL, MySQL), vector (Pinecone, Weaviate, pgvector), and graph (Neo4j, Neptune) — applying the right tool to each access pattern, not the most fashionable one. Set technical direction: write design docs, make build-vs-buy calls, and defend your approach with evidence rather than instinct. Work across the stack when the problem demands it — services, data access, infrastructure-as-code, CI/CD — and diagnose it when things drift in production. Raise the floor for the team: mentor mid-level and junior engineers, run rigorous code reviews, and hold the quality bar without making it someone else's job to ask you.