Staff Data Engineer (Databricks specialist)

Jobgether; Switzerland; Remote; CHF 180,000 - 240,000; Full time

Benefits offered by this job

Fully remote work

Work‑life balance

Continuous learning

International collaboration

Career growth

Full-time opportunity

Job summary

Jobgether, partnering with a Switzerland-based client, seeks a Staff Data Engineer (Databricks Specialist) to lead enterprise data platforms. You will architect scalable pipelines, drive MDM efforts, and integrate agentic AI, RAG, and LLM-powered solutions across cloud environments.

You will mentor engineers, define data governance, and collaborate with product and analytics leaders to align data roadmaps with AI-driven opportunities. Fully remote role with international collaboration.

Qualifications

• Bachelor's degree in Computer Science, Engineering, MIS, or related discipline is preferred.
• 12+ years of professional experience in data engineering or software engineering with enterprise-scale data platforms.
• Expert SQL and strong Python; Scala/Java a plus.
• Hands-on Databricks and cloud data platforms (Snowflake, Azure Synapse, BigQuery, Redshift).
• Strong cloud-native, distributed systems, data modeling (dimensional, Data Vault, Lakehouse), ETL/ELT know-how.
• Experience with enterprise MDM including entity resolution, reference data, hierarchies; familiarity with Informatica, Reltio, Profisee.

Responsibilities

• Own and drive the technical architecture of cross-functional data initiatives.
• Design and maintain scalable data pipelines and platforms using Databricks and cloud-native tech.
• Lead MDM initiatives including golden records and hierarchies, data governance, and data stewardship.
• Architect AI/LLM-powered solutions and RAG pipelines into platforms.
• Build data foundations for ML/AI, including feature stores and vector databases.
• Develop backend data services and APIs (REST/GraphQL).
• Set engineering standards for data modeling, quality, testing, observability, CI/CD, and responsible AI.

Skills

Databricks

Python

SQL

Cloud platforms

Data modeling

AI/LLMOps

REST/GraphQL

Education

Bachelor's degree in CS/Engineering

Tools

Databricks

Snowflake

Azure Synapse

BigQuery

Redshift

Kubernetes

Airflow

Job description

This 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 Switzerland.

We 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.

Accountabilities:

• Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
• Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
• Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
• Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
• Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
• Design and deliver backend data services and APIs using REST or GraphQL, ensuring curated datasets are accessible to applications, analytics, and business intelligence platforms.
• Establish and promote engineering standards covering data modeling, quality, testing, observability, CI/CD, deployment, and responsible AI-assisted development.
• Implement enterprise-wide data governance, lineage, cataloging, and quality frameworks to ensure data consistency, reliability, and transparency.
• Lead technical design reviews, provide architectural guidance, and influence engineering decisions across multiple teams.
• Collaborate with product, analytics, and engineering leadership to define scalable data roadmaps aligned with business objectives and AI-driven opportunities.
• Identify and resolve systemic performance, reliability, and scalability challenges across the data ecosystem.
• Mentor and coach senior and mid-level engineers, promoting technical excellence and best practices in data engineering, MDM, and AI.
• Evaluate emerging technologies, frameworks, and industry trends to continuously improve platform capabilities, engineering efficiency, and resilience.

Requirements:

• Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline is preferred.
• 12+ years of professional experience in data engineering or software engineering, with a proven track record of architecting enterprise-scale data platforms and pipelines.
• Expert-level SQL skills and strong proficiency in Python. Experience with Scala or Java is an advantage.
• Extensive hands‑on experience with Databricks and modern cloud data platforms such as Snowflake, Azure Synapse, BigQuery, or Amazon Redshift.
• Strong understanding of cloud-native architecture, distributed systems, data modeling methodologies (dimensional modeling, Data Vault, Lakehouse), and ETL/ELT architectures.
• Proven 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.
• Practical 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.
• Experience developing backend data services and REST/GraphQL APIs, with the ability to contribute across the technology stack.
• Strong knowledge of relational and NoSQL databases, as well as modern data lake and lakehouse formats such as Delta Lake, Apache Iceberg, and Parquet.
• Deep understanding of CI/CD pipelines, Infrastructure as Code (IaC), DevOps, and DataOps practices.
• Demonstrated ability to lead large‑scale technical initiatives involving multiple teams and stakeholders.
• Excellent communication, collaboration, and problem‑solving skills, with the ability to influence technical decisions without direct managerial authority.
• Proven mentoring and coaching capabilities, with a passion for developing engineering talent and promoting knowledge sharing.

Nice to have:

• Experience with data governance, lineage, and cataloging solutions such as Unity Catalog, Microsoft Purview, Collibra, or Alation.
• Knowledge of multi-agent architectures, tool-calling systems, and advanced RAG implementations.
• Experience with event-driven architectures and real-time data processing technologies such as Kafka, Azure Event Hubs, Amazon Kinesis, Flink, or Spark Structured Streaming.
• Experience building data layers for AI and machine learning, including feature stores, vector databases, embeddings, and ML/LLMOps solutions.
• Familiarity with containerization and orchestration technologies such as Docker and Kubernetes, as well as workflow orchestration tools like Airflow, Dagster, and dbt.
• Previous experience in commercial real estate, fintech, or complex operational and transaction-based environments.
• Technical thought leadership demonstrated through conference speaking, technical publications, or open-source contributions, particularly in applied AI and modern data engineering.

Benefits:

• Fully remote work: Enjoy the flexibility to work from anywhere, with a digital‑first approach and opportunities to collaborate with international teams.
• Work‑life balance: Benefit from flexible working arrangements that support your personal and professional commitments.
• Meaningful projects: Contribute to challenging, high‑impact projects across different industries and work with modern technologies.
• Continuous learning: Access ongoing professional development opportunities, hands‑on technical challenges, and opportunities to expand your expertise.
• International collaboration: Join a diverse, multicultural team of talented professionals who value creativity, innovation, and knowledge sharing.
• Career growth: Develop your technical leadership capabilities and help shape the future of enterprise data engineering and AI‑driven solutions.
• Full‑time opportunity: Join a team working on long‑term, impactful data engineering initiatives.