Description

You will design, build, and operate scalable data services for analytics, AI, and GenAI use cases.

This role is on-site.

Responsibilities

• Build and maintain scalable data pipelines and ingestion frameworks for batch, streaming, and event-driven data using Spark, SQL, and Python.
• Develop modular data models and semantic layers optimized for BI self-service and AI applications, leveraging Databricks and Delta Lake.
• Implement orchestration workflows (e.g., Databricks Workflows) and ensure data quality, lineage, and observability using tools like Great Expectations.
• Deliver curated datasets and reusable data assets for analytics and GenAI, including support for embeddings and RAG pipelines.
• Collaborate with AI Engineers and Data Scientists to enable self-service data products and adhere to data governance standards.

Required Skills

• 9+ years of experience in data engineering and building production-grade data pipelines.
• Strong hands-on experience with Databricks, Spark, SQL, and Python.
• Experience with storage technologies such as Delta Lake, ADLS, and vector stores.
• Proficiency in data quality tooling, observability practices, and CI/CD workflows.
• Knowledge of data modeling, orchestration frameworks, and data governance (e.g., GDPR).
• Ability to process structured, graph, and unstructured data for AI use cases.

Preferred Skills

• Experience working in a global, multi-team environment.
• Exposure to unstructured data processing and AI/GenAI data pipelines.

Key Skills

Education

Bachelor's degree