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Build scalable ETL/ELT pipelines in Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, and Unity Catalog. Ingest batch and streaming data across medallion architecture layers, develop transformations and data quality rules, and implement orchestration, monitoring, alerting, and automation. The role requires cloud data engineering, data modeling, performance tuning, CI/CD, and strong knowledge of lakehouse architecture, governance, and distributed systems.

Designs and builds secure, production-grade Generative AI and agentic AI applications using Python, LLMs, cloud services, and AI frameworks. Responsibilities include developing scalable AI pipelines, integrating Databricks data with AWS services, applying RAG and vector databases, evaluating models, implementing guardrails, researching emerging tools, improving application infrastructure, and guiding team members.

Build scalable production-grade ETL/ELT pipelines on Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, and Workflows. Develop data ingestion, transformations, quality rules, orchestration, monitoring, and automation across Bronze, Silver, and Gold layers. Apply dimensional data modeling, streaming, cloud platforms, CI/CD, governance, security, performance tuning, and lakehouse architecture. Explain technical concepts and architecture decisions using real project examples.

Designs and leads enterprise Snowflake data architectures, cloud data platforms, ELT/ETL pipelines, and AI-powered applications. Builds RAG, semantic search, Cortex Agents, vector embedding, and Cortex AI Function solutions using Snowpark, Python, SQL, and dbt. Establishes secure, governed, and cost-optimized data environments, mentors engineering teams, leads architecture reviews, and partners with stakeholders on scalable data and generative AI solutions.

Analyze insurance policy, premium, claims, billing, and financial data; build KPI dashboards and reports; write and optimize SQL; validate and model datasets; identify trends, anomalies, fraud indicators, and leakage; and translate business questions into clear recommendations for underwriting, actuarial, claims, finance, and other stakeholders. The role supports pricing, reserving, portfolio management, retention, and loss-ratio analyses.

Lead end-to-end AI/ML, data, and ETL architecture for regulated insurance platforms. Design lakehouse and Medallion architectures, feature stores, MLOps and GenAI/RAG solutions, scalable batch and streaming pipelines, canonical and dimensional data models, governance controls, security, lineage, and compliance frameworks. Guide technology selection, performance and cost optimization, engineering standards, and architecture reviews with senior client stakeholders.

Designs and builds secure, production-grade GenAI and agentic AI applications using Python, LLMs, AWS, and Databricks. Develops scalable AI pipelines, integrates data and cloud services, applies RAG, vector databases, model evaluation, and guardrails, and creates cost-effective solutions. The engineer researches emerging tools, improves application infrastructure and performance, works independently, guides team members, and delivers under tight timelines.