We are seeking a highly skilled Manager – AI & Data (Context Layer) to lead the design, implementation, and governance of enterprise data context layers that power AI, analytics, and decision intelligence solutions. The ideal candidate will possess strong expertise in Databricks or Snowflake, data management, data lakes, data warehouses, ETL/ELT pipelines, semantic data modeling, and AI-ready data architectures.
This role will be responsible for creating trusted, business-aligned data foundations that enable advanced analytics, GenAI, agentic AI, and enterprise reporting use cases. The candidate will collaborate closely with business stakeholders, solution architects, data engineers, AI teams, and product owners to establish scalable and reusable data assets.
Key Responsibilities
Data Context Layer Strategy & Architecture
- Design and implement enterprise data context layers that provide business-ready, AI-consumable datasets.
- Define semantic and logical data models to support analytics, AI, and operational reporting.
- Establish data domains, business glossaries, metadata standards, and knowledge representations.
- Enable unified access to data across structured, semi-structured, and unstructured sources.
Data Platform Engineering
- Architect and implement solutions using Databricks or Snowflake.
- Build and optimize data lake, lakehouse, and data warehouse architectures.
- Design scalable ETL/ELT pipelines for ingestion, transformation, enrichment, and consumption.
- Drive modernization from legacy data warehouses to cloud-native platforms.
AI & GenAI Enablement
- Create AI-ready datasets and knowledge layers supporting GenAI and agentic AI applications.
- Collaborate with AI teams to develop retrieval, grounding, and context management strategies.
- Implement data preparation frameworks supporting LLM-based use cases.
- Support Vector DB, RAG, knowledge graph, and semantic search initiatives where applicable.
Data Governance & Quality
- Establish data quality, lineage, cataloging, and governance frameworks.
- Define data stewardship processes and governance controls.
- Ensure compliance with security, privacy, and regulatory requirements.
- Monitor data health, accuracy, completeness, and consistency.
Leadership & Stakeholder Management
- Lead cross-functional teams of data engineers, architects, and analysts.
- Engage with business stakeholders to translate requirements into scalable data solutions.
- Provide technical leadership and mentoring to engineering teams.
- Drive delivery excellence through Agile methodologies and best practices.
Required Experience
- 7–10+ years of experience in Data Engineering, Data Architecture, Data Management, or Data Warehousing.
- 3+ years leading enterprise-scale cloud data platform initiatives.
- Experience designing business-facing semantic or context layers.
Technical Skills
- Strong expertise in Databricks or Snowflake.
- Deep understanding of: Data Lakes, Lakehouse Architecture, Data Warehouses, Data Mesh, Data Fabric concepts
- Experience with ETL/ELT tools such as: Databricks Workflows, Informatica, dbt, Apache Airflow
- Strong SQL and data modeling skills, Python, PySpark, or Scala.
- Knowledge of metadata management and data catalog tools.
Leadership Skills
- Strong stakeholder management and communication skills.
- Ability to translate business problems into data and AI solutions.
- Experience leading distributed delivery teams.
Preferred Qualifications
- Experience implementing AI/GenAI solutions on Databricks Mosaic AI, Azure AI, OpenAI, or equivalent platforms.
- Knowledge of RAG (Retrieval Augmented Generation), Vector Databases, and Knowledge Graphs.
- Experience with Unity Catalog, Snowflake Horizon, Collibra, or Alation.
- Exposure to Agentic AI frameworks and AI governance.
- Experience in consulting, life sciences, healthcare, or enterprise data transformation programs.
