Your experience:5+ years of experience in data engineering, AI engineering, analytics engineering, or cloud data platform delivery.
Strong SQL and Python skills.
Experience with agentic development practices, including the use of AI agents and coding assistants to accelerate solution design, prototyping, code generation, testing, documentation, deployment preparation, and iterative delivery of data and AI products.
Hands-on experience with Databricks, including data modeling, semantic layer design, data pipelines, performance tuning, and platform optimization.
Strong knowledge of Databricks Genie and the data modeling, metadata, governance, and Unity Catalog foundations required to enable natural-language analytics.
Deep understanding of semantic layer modeling, business metrics, KPI definitions, hierarchies, dimensions, governed datasets, and AI-ready data products.
Experience with data catalogues, metadata management, lineage, data ownership, business glossary, and data quality controls.
Experience with structured and unstructured data, including enterprise tables, documents, PDFs, SharePoint/Teams content, logs, and business metadata.
Strong understanding of Databricks Clusters / SQL Warehouses, including sizing, workload isolation, autoscaling, performance tuning, and cost optimization.
Experience with LLM model selection and cost optimization, including model evaluation, token usage, latency, context window, inference cost, and quality/cost trade-offs.
Ability to assess and optimize LLM usage costs, including token consumption, caching strategies, prompt optimization, model routing, use-case-based model selection, inference cost monitoring, and balancing quality vs. cost.
Practical experience with GenAI, AI agents, RAG, enterprise search, conversational analytics, or LLM-powered assistants.
Strong knowledge of data governance, RBAC/RLS, masking, auditability, and secure enterprise data access.
Strong communication skills, with the ability to explain technical decisions to data engineers, architects, product owners, governance teams, and business users.
Experience in regulated enterprise environments where security, compliance, auditability, and data governance are critical.
Responsibilities
Your experience:5+ years of experience in data engineering, AI engineering, analytics engineering, or cloud data platform delivery.
Strong SQL and Python skills.
Experience with agentic development practices, including the use of AI agents and coding assistants to accelerate solution design, prototyping, code generation, testing, documentation, deployment preparation, and iterative delivery of data and AI products.
Hands-on experience with Databricks, including data modeling, semantic layer design, data pipelines, performance tuning, and platform optimization.
Strong knowledge of Databricks Genie and the data modeling, metadata, governance, and Unity Catalog foundations required to enable natural-language analytics.
Deep understanding of semantic layer modeling, business metrics, KPI definitions, hierarchies, dimensions, governed datasets, and AI-ready data products.
Experience with data catalogues, metadata management, lineage, data ownership, business glossary, and data quality controls.
Experience with structured and unstructured data, including enterprise tables, documents, PDFs, SharePoint/Teams content, logs, and business metadata.
Strong understanding of Databricks Clusters / SQL Warehouses, including sizing, workload isolation, autoscaling, performance tuning, and cost optimization.
Experience with LLM model selection and cost optimization, including model evaluation, token usage, latency, context window, inference cost, and quality/cost trade-offs.
Ability to assess and optimize LLM usage costs, including token consumption, caching strategies, prompt optimization, model routing, use-case-based model selection, inference cost monitoring, and balancing quality vs. cost.
Practical experience with GenAI, AI agents, RAG, enterprise search, conversational analytics, or LLM-powered assistants.
Strong knowledge of data governance, RBAC/RLS, masking, auditability, and secure enterprise data access.
Strong communication skills, with the ability to explain technical decisions to data engineers, architects, product owners, governance teams, and business users.
Experience in regulated enterprise environments where security, compliance, auditability, and data governance are critical.
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