Enterprise Data Architect- AWS

Job Title: Enterprise Data Architect (AWS)

Experience: 12+ years

Location: Bangalore

Employment Type: Full-time

Notice- Prefer with shorter notice or can join within 30 days

About the Role:

cloud-native data architecture (AWS) and data engineering frameworks (Databricks, Spark)

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 experiencewith 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.
10. Excellent communication and leadership skills to collaborate with technical and business teams.
11. Certifications in AWS (Data Analytics / Solutions Architect) or Databricks preferred.

Preferred Qualifications:

• Experience in enterprise data strategy, governance frameworks, and migration of legacy systems to cloud.
• Exposure to GenAI data pipelines or LLM-based data preparation workflows.
• Strong background in data security, IAM, and compliance standards.

Education:

Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or related field.