Enterprise Data Architect

About the Role:

Enterprise Data Architect

Key Responsibilities:

1. Lead the enterprise data architecture strategy, ensuring scalability, performance, and alignment with business goals.
2. Architect and implement data lakehouse solutions using Databricks on AWS for unified data management and analytics.
3. Design end-to-end data pipelines, integration frameworks, and governance models across structured and unstructured data sources.
4. Define data models, metadata management, and data quality frameworks for enterprise-wide adoption.
5. Collaborate with data engineering, AI/ML, analytics, and business teams to enable real-time and batch data processing.
6. Evaluate and integrate emerging technologies in data mesh, GenAI data pipelines, and automation frameworks.
7. Provide technical leadership and mentorship to data engineering and architecture teams.
8. Establish best practices for data security, lineage, compliance (GDPR, HIPAA), and cloud cost optimization.
9. Partner with business stakeholders to define data modernization roadmaps and cloud migration strategies.

Required Skills and Experience:

1. 10-15 years IT experience with 5-8 years enterprise data architect
2. Strong experience in Data Architecture, Data Engineering, or related domains.
3. Proven experience architecting enterprise-scale data platforms using AWS (S3, Glue, Lambda, Redshift, EMR, Athena, Lake Formation, etc.).
4. Hands-on expertise in Databricks (Delta Lake, Spark, Unity Catalog, MLflow).
5. Strong experience with data modeling (dimensional, canonical, semantic models) and ETL/ELT pipelines.
6. Deep understanding of data governance, master data management (MDM), and data cataloging tools.
7. Proficient in SQL, Python, PySpark, and API-based data integration.
8. Experience with modern data stack (Snowflake, dbt, Airflow, Kafka, etc.) is a plus.
9. Strong understanding of AI/ML data readiness, metadata design, and data observability frameworks.