What you will be doing:

• Design, build, and maintain scalable data pipelines and lakehouse structures
• Deliver data solutions supporting analytics, BI, machine learning, and Generative AI applications
• Apply enterprise data lake and lakehouse principles to ensure solutions are reliable, secure, governed, and fit for downstream consumption
• Translate business and analytical requirements into production-ready data solutions
• Build and operate solutions using Databricks, including Delta Lake, Databricks Jobs & Workflows, Unity Catalog, Databricks Bundles, notebooks, and shared libraries
• Enable data consumption for GenAI use cases such as RAG, AI services, and agent workflows
• Support analytics platforms, reporting tools, and downstream operational systems
• Build data pipelines for Generative AI applications, including curated knowledge datasets, structured and semi-structured data, metadata, and lineage management
• Enable GenAI data patterns including Retrieval Augmented Generation (RAG), prompt/context preparation, and AI model input/output flows
• Work closely with AI Engineers and Product Owners to align engineering deliverables to AI and GenAI use cases
• Develop production-grade pipelines using Python, PySpark, SQL, and Apache Spark
• Implement automated testing and CI/CD practices for data engineering workloads
• Ensure data solutions are observable, resilient, performant, and cost-efficient
• Support operational stability, incident resolution, and root cause analysis
• Collaborate within Agile, cross-functional product squads alongside AI/ML engineers, analytics teams, platform teams, and security stakeholders
• Contribute to engineering reviews, standards, and design discussions
• Maintain documentation, operational runbooks, and governance compliance

What we are looking for:

• Relevant Degree or Diploma in Computer Science, Information Technology, Data Engineering, or related field
• 6+ years’ experience as a Senior / Lead Data Engineer
• 2+ years’ hands-on experience working in Databricks environments
• Strong understanding of enterprise data lake and lakehouse architecture
• Strong proficiency in Python, SQL, and Apache Spark
• Experience building and operating production-grade data platforms
• Experience working in enterprise or regulated environments
• Strong understanding of data governance, security, and operational best practices
• Experience working in Agile, product-aligned squads
• Strong analytical and problem-solving skills
• Excellent collaboration and communication skills

Advantageous:

• Experience supporting AI, ML, or Generative AI workloads from a data engineering perspective
• Familiarity with RAG data patterns and AI-serving datasets
• Exposure to vector or embedding-ready data workflows
• Cloud-native data platform experience (AWS or Azure)
• Experience supporting analytics and AI operational workloads at scale

Please note if you do not hear from us within 3 weeks, please consider your application unsuccessful.

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