Staff Data Engineer (Databricks specialist)

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.