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:
We are seeking a seasoned Enterprise Data Architect with over 12+ years of experience in designing, implementing, and optimizing enterprise data platforms. The ideal candidate will have deep expertise in cloud-native data architecture (AWS) and data engineering frameworks (Databricks, Spark) to drive large-scale digital transformation and AI/analytics initiatives.
Key Responsibilities:
- Lead the enterprise data architecture strategy, ensuring scalability, performance, and alignment with business goals.
- Architect and implement data lakehouse solutions using Databricks on AWS for unified data management and analytics.
- Design end-to-end data pipelines, integration frameworks, and governance models across structured and unstructured data sources.
- Define data models, metadata management, and data quality frameworks for enterprise-wide adoption.
- Collaborate with data engineering, AI/ML, analytics, and business teams to enable real-time and batch data processing.
- Evaluate and integrate emerging technologies in data mesh, GenAI data pipelines, and automation frameworks.
- Provide technical leadership and mentorship to data engineering and architecture teams.
- Establish best practices for data security, lineage, compliance (GDPR, HIPAA), and cloud cost optimization.
- Partner with business stakeholders to define data modernization roadmaps and cloud migration strategies.
Required Skills and Experience:
- 10-15 years IT experiencewith 5-8 years enterprise data architect
- Strong experience in Data Architecture, Data Engineering, or related domains.
- Proven experience architecting enterprise-scale data platforms using AWS (S3, Glue, Lambda, Redshift, EMR, Athena, Lake Formation, etc.).
- Hands-on expertise in Databricks (Delta Lake, Spark, Unity Catalog, MLflow).
- Strong experience with data modeling (dimensional, canonical, semantic models) and ETL/ELT pipelines.
- Deep understanding of data governance, master data management (MDM), and data cataloging tools.
- Proficient in SQL, Python, PySpark, and API-based data integration.
- Experience with modern data stack (Snowflake, dbt, Airflow, Kafka, etc.) is a plus.
- Strong understanding of AI/ML data readiness, metadata design, and data observability frameworks.
- Excellent communication and leadership skills to collaborate with technical and business teams.
- 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.
