Senior Data Engineer

ppAt CluePoints, we're redefining how clinical trials are run. As a leading providerof Risk-Based Quality Management and Data Quality Oversight software, we useadvanced statistics, artificial intelligence and machine learning to improve thequality, accuracy and integrity of clinical trial data — turning artificialintelligence into human intelligence.
We're an ambitious, fast-growing technology company with a dynamic anddiverse international team. Collaboration, flexibility and continuous learning arepart of our DNA. Guided by our values of Care, Passion and Smart Disruption,we're united by a shared mission: to create smarter ways to run efficient clinicaltrials and deliver AI-powered insights that improve human outcomes worldwide. /p h3The Role /h3 pWe're looking for a hands-on Senior Data Engineer to own the technical design, reliability and evolution of our Databricks data foundation. You'll work alongside Clinical Data Insights Analysts — colleagues with strong business, clinical and BI expertise who also write SQL and Python — complementing their skills with deeper engineering, automation and production-grade rigour. /p h3Job requirements /h3 ul li5+ years in data engineering or data-platform engineering. /li liStrong hands-on Databricks experience (or comparable cloud lakehouse platform). /li liAdvanced SQL and strong Python / PySpark skills. /li liProduction-grade pipeline experience — design, build, test, deploy and maintain. /li liSolid understanding of Delta Lake, medallion architecture, and OLAP/OLTP transformation. /li liExperience with data governance, Unity Catalog (or equivalent), automated quality controls and CI/CD. /li liAt least one major cloud platform: Azure, AWS or GCP. /li liPractical use of generative AI in engineering workflows, with solid critical judgement on its outputs. /li liHigh autonomy — you take ownership from design through deployment, monitoring and maintenance. /li liCollaborative by nature; comfortable working directly alongside analysts who also write code. /li /ul ul li5+ years in data engineering or data-platform engineering. /li liStrong hands-on Databricks experience (or comparable cloud lakehouse platform). /li liAdvanced SQL and strong Python / PySpark skills. /li liProduction-grade pipeline experience — design, build, test, deploy and maintain. /li liSolid understanding of Delta Lake, medallion architecture, and OLAP/OLTP transformation. /li liExperience with data governance, Unity Catalog (or equivalent), automated quality controls and CI/CD. /li liAt least one major cloud platform: Azure, AWS or GCP. /li liPractical use of generative AI in engineering workflows, with solid critical judgement on its outputs. /li liHigh autonomy — you take ownership from design through deployment, monitoring and maintenance. /li liCollaborative by nature; comfortable working directly alongside analysts who also write code. /li /ul h3Nice to Have: /h3 ul liLakeflow Jobs / Lakeflow Declarative Pipelines; Databricks Asset Bundles. /li liDatabricks AI/BI, Genie, conversational analytics or AI agent experience. /li liDatabricks Data Engineer certification. /li liExperience in a regulated environment (clinical trials, life sciences or similar). /li /ul h3Tech Stack: /h3 ul liDatabricks /li liDelta Lake /li liUnity Catalog /li liLakeflow Jobs Declarative Pipelines /li liMedallion architecture /li liSQL / Python / PySpark /li liMicrosoft Azure /li liGit / CI/CD /li liDatabricks Asset Bundles /li liDatabricks AI/BI /li liGenie /li /ul h3Job responsibilities /h3 ul liOwn the architecture, reliability and governance of the team's Databricks lakehouse (Delta Lake, medallion architecture, Unity Catalog). /li liDesign and operate batch and streaming ingestion pipelines from the CluePoints platform and other business systems (e.g. Zendesk). /li liCo-develop SQL, Python and PySpark transformations with the analyst team; review and optimize for performance, scalability and cost. /li liProductionize analytical and AI prototypes — adding testing, orchestration, monitoring, error handling and deployment controls. /li liBuild automated data-quality controls, monitoring and alerting; investigate and resolve pipeline incidents. /li liImplement technical governance: naming conventions, metadata, lineage, tagging and access controls via Unity Catalog. /li liApply AI and automation across the data lifecycle — code development, documentation, metadata classification and inconsistency detection. /li liCollaborate with Engineering on source-system access and with Product when Databricks insights are candidates for platform integration. /li liHealth Insurance through Alan (100% hospitalisation cover, 80% ambulatory and dental) /li liMobility Budget for eco-transport, housing, or car allowance (flexible 3-pillar system) /li liGroup Insurance Plan with 6–12% employer pension contribution based on seniority /li liMeal Vouchers (€8/day) and Eco Vouchers for sustainable purchases /li liA hub-based hybrid model