Staff Data Engineer (Databricks specialist)
pbThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Engineer (Databricks Specialist) based in Italy. /b /p pWe are seeking an experienced Staff Data Engineer to lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies. In this strategic role, you will drive complex, cross-functional data initiatives, ensuring reliable data ingestion, transformation, storage, and delivery. You will play a key role in shaping enterprise data architecture, Master Data Management (MDM), and AI-powered data capabilities. Working closely with engineering, product, and analytics leaders, you will translate business needs into innovative and scalable technical solutions. You will also champion engineering excellence, data governance, and platform performance while mentoring experienced engineers. This is an opportunity to influence technical direction, accelerate AI adoption, and build the foundations for next-generation data-driven solutions. /p h3Accountabilities: /h3 ul liOwn and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers. /li liDesign, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP. /li liLead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship. /li liArchitect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms. /li liBuild and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines. /li liDesign and deliver backend data services and APIs using REST or GraphQL, ensuring curated datasets are accessible to applications, analytics, and business intelligence platforms. /li liEstablish and promote engineering standards covering data modeling, quality, testing, observability, CI/CD, deployment, and responsible AI-assisted development. /li liImplement enterprise-wide data governance, lineage, cataloging, and quality frameworks to ensure data consistency, reliability, and transparency. /li liLead technical design reviews, provide architectural guidance,
and influence engineering decisions across multiple teams. /li liCollaborate with product, analytics, and engineering leadership to define scalable data roadmaps aligned with business objectives and AI-driven opportunities. /li liIdentify and resolve systemic performance, reliability, and scalability challenges across the data ecosystem. /li liMentor and coach senior and mid-level engineers, promoting technical excellence and best practices in data engineering, MDM, and AI. /li liEvaluate emerging technologies, frameworks, and industry trends to continuously improve platform capabilities, engineering efficiency, and resilience. /li /ul h3Requirements: /h3 ul liBachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline is preferred. /li li12+ years of professional experience in data engineering or software engineering, with a proven track record of architecting enterprise-scale data platforms and pipelines. /li liExpert-level SQL skills and strong proficiency in Python. Experience with Scala or Java is an advantage. /li liExtensive hands‑on experience with Databricks and modern cloud data platforms such as Snowflake, Azure Synapse, BigQuery, or Amazon Redshift. /li liStrong understanding of cloud-native architecture, distributed systems, data modeling methodologies (dimensional modeling, Data Vault, Lakehouse), and ETL/ELT architectures. /li liProven experience designing and implementing enterprise MDM solutions, including entity resolution, match-and-merge, golden records, reference data, and hierarchy management. Familiarity with Informatica, Reltio, Profisee, or similar platforms is highly valued. /li liPractical experience building or integrating agentic AI systems, LLM-powered applications, RAG pipelines, and AI agent orchestration frameworks such as LangChain, AutoGen, Semantic Kernel, or MCP. /li liExperience developing backend data services and REST/GraphQL APIs, with the ability to contribute across the technology stack. /li liStrong knowledge of relational and NoSQL databases, as well as modern data lake and lakehouse formats such as Delta Lake, Apache Iceberg, and Parquet. /li liDeep understanding of CI/CD pipelines, Infrastructure as Code (IaC),
DevOps, and DataOps practices. /li liDemonstrated ability to lead large-scale technical initiatives involving multiple teams and stakeholders. /li liExcellent communication, collaboration, and problem-solving skills, with the ability to influence technical decisions without direct managerial authority. /li liProven mentoring and coaching capabilities, with a passion for developing engineering talent and promoting knowledge sharing. /li /ul h3Nice to have: /h3 ul liExperience with data governance, lineage, and cataloging solutions such as Unity Catalog, Microsoft Purview, Collibra, or Alation. /li liKnowledge of multi‑agent architectures, tool‑calling systems, and advanced RAG implementations. /li liExperience with event‑driven architectures and real‑time data processing technologies such as Kafka, Azure Event Hubs, Amazon Kinesis, Flink, or Spark Structured Streaming. /li liExperience building data layers for AI and machine learning, including feature stores, vector databases, embeddings, and ML/LLMOps solutions. /li liFamiliarity with containerization and orchestration technologies such as Docker and Kubernetes, as well as workflow orchestration tools like Airflow, Dagster, and dbt. /li liPrevious experience in commercial real estate, fintech, or complex operational and transaction‑based environments. /li liTechnical thought leadership demonstrated through conference speaking, technical publications, or open‑source contributions, particularly in applied AI and modern data engineering. /li /ul h3Benefits: /h3 ul liFully remote work: Enjoy the flexibility to work from anywhere, with a digital‑first approach and opportunities to collaborate with international teams. /li liWork‑life balance: Agevolazioni from flexible working arrangements that support your personal and professional commitments. /li liMeaningful projects: Contribute to challenging, high‑impact projects across different industries and work with modern technologies. /li liContinuous learning: Access ongoing professional development opportunities, hands‑on technical challenges, and opportunities to expand your expertise. /li liInternational collaboration: Join a diverse, multicultural team of talented professionals who value creativity, innovation, and knowledge sharing. /li liCareer growth: Develop your technical leadership capabilities and help shape the future of enterprise data engineering and AI‑driven solutions. /li liFull‑time opportunity: Join a team working on long‑term, impactful data engineering initiatives. /li /ul #J-18808-Ljbffr
and influence engineering decisions across multiple teams. /li liCollaborate with product, analytics, and engineering leadership to define scalable data roadmaps aligned with business objectives and AI-driven opportunities. /li liIdentify and resolve systemic performance, reliability, and scalability challenges across the data ecosystem. /li liMentor and coach senior and mid-level engineers, promoting technical excellence and best practices in data engineering, MDM, and AI. /li liEvaluate emerging technologies, frameworks, and industry trends to continuously improve platform capabilities, engineering efficiency, and resilience. /li /ul h3Requirements: /h3 ul liBachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline is preferred. /li li12+ years of professional experience in data engineering or software engineering, with a proven track record of architecting enterprise-scale data platforms and pipelines. /li liExpert-level SQL skills and strong proficiency in Python. Experience with Scala or Java is an advantage. /li liExtensive hands‑on experience with Databricks and modern cloud data platforms such as Snowflake, Azure Synapse, BigQuery, or Amazon Redshift. /li liStrong understanding of cloud-native architecture, distributed systems, data modeling methodologies (dimensional modeling, Data Vault, Lakehouse), and ETL/ELT architectures. /li liProven experience designing and implementing enterprise MDM solutions, including entity resolution, match-and-merge, golden records, reference data, and hierarchy management. Familiarity with Informatica, Reltio, Profisee, or similar platforms is highly valued. /li liPractical experience building or integrating agentic AI systems, LLM-powered applications, RAG pipelines, and AI agent orchestration frameworks such as LangChain, AutoGen, Semantic Kernel, or MCP. /li liExperience developing backend data services and REST/GraphQL APIs, with the ability to contribute across the technology stack. /li liStrong knowledge of relational and NoSQL databases, as well as modern data lake and lakehouse formats such as Delta Lake, Apache Iceberg, and Parquet. /li liDeep understanding of CI/CD pipelines, Infrastructure as Code (IaC),
DevOps, and DataOps practices. /li liDemonstrated ability to lead large-scale technical initiatives involving multiple teams and stakeholders. /li liExcellent communication, collaboration, and problem-solving skills, with the ability to influence technical decisions without direct managerial authority. /li liProven mentoring and coaching capabilities, with a passion for developing engineering talent and promoting knowledge sharing. /li /ul h3Nice to have: /h3 ul liExperience with data governance, lineage, and cataloging solutions such as Unity Catalog, Microsoft Purview, Collibra, or Alation. /li liKnowledge of multi‑agent architectures, tool‑calling systems, and advanced RAG implementations. /li liExperience with event‑driven architectures and real‑time data processing technologies such as Kafka, Azure Event Hubs, Amazon Kinesis, Flink, or Spark Structured Streaming. /li liExperience building data layers for AI and machine learning, including feature stores, vector databases, embeddings, and ML/LLMOps solutions. /li liFamiliarity with containerization and orchestration technologies such as Docker and Kubernetes, as well as workflow orchestration tools like Airflow, Dagster, and dbt. /li liPrevious experience in commercial real estate, fintech, or complex operational and transaction‑based environments. /li liTechnical thought leadership demonstrated through conference speaking, technical publications, or open‑source contributions, particularly in applied AI and modern data engineering. /li /ul h3Benefits: /h3 ul liFully remote work: Enjoy the flexibility to work from anywhere, with a digital‑first approach and opportunities to collaborate with international teams. /li liWork‑life balance: Agevolazioni from flexible working arrangements that support your personal and professional commitments. /li liMeaningful projects: Contribute to challenging, high‑impact projects across different industries and work with modern technologies. /li liContinuous learning: Access ongoing professional development opportunities, hands‑on technical challenges, and opportunities to expand your expertise. /li liInternational collaboration: Join a diverse, multicultural team of talented professionals who value creativity, innovation, and knowledge sharing. /li liCareer growth: Develop your technical leadership capabilities and help shape the future of enterprise data engineering and AI‑driven solutions. /li liFull‑time opportunity: Join a team working on long‑term, impactful data engineering initiatives. /li /ul #J-18808-Ljbffr