Enterprise Data Architect – Job Description Location: Bangalore Experience: 15+ years in Data Engineering & Data Platforms Employment Type: Full-time About Aptus Data Labs Aptus Data Labs is a global Data Engineering and AI solutions partner helping enterprises build modern, scalable, and intelligence-driven organizations. With deep expertise across cloud platforms, advanced analytics, AI/ML, and enterprise data transformation, we empower businesses to unlock the full value of their data. Our focus on innovation, domain excellence, and engineering quality enables us to deliver high-impact platforms—ranging from enterprise data lakes to AI-driven automation and industry-specific solutions. Trusted by leading companies across the US, India, Africa, and Europe, Aptus Data Labs is committed to shaping future-ready digital ecosystems that drive growth, efficiency, and strategic advantage. About the Role We are seeking a highly accomplished Enterprise Data Architect with deep expertise in designing modern data platforms, integrating complex enterprise datasets, and driving large-scale digital and AI initiatives. The ideal candidate brings 15+ years of strong experience in data engineering, data platforms, data governance, data integration, and data operations, with 5+ years of hands-on Databricks Lakehouse implementation on AWS and strong Reltio MDM experience. This role is instrumental in shaping the enterprise data foundation, leading multi-domain integrations, and enabling AI-ready architectures across global teams in the US, India, and Ireland. Key Responsibilities 1. Lead the enterprise data architecture strategy, focusing on scalability, modernization, interoperability, and business alignment. 2. Architect and operationalize Databricks Lakehouse solutions on AWS, including ingestion, transformation, orchestration, governance, and consumption layers. 3. Design and implement Medallion architecture (Bronze–Silver–Gold) with 100+ source integrations using Boomi Integrator and Databricks pipelines. 4. Drive enterprise Master Data Management (MDM) using Reltio, including entity modeling, data quality, match-merge rules, survivorship, workflows, and golden record stewardship. 5. Establish frameworks for metadata management, data quality, lineage, cataloging, and governance, leveraging Unity Catalog and AWS-native security. 6. Enable AI and analytics teams by building AI-ready datasets, feature stores, and GenAI-supporting data pipelines. 7. Provide leadership, mentorship, and architectural oversight for data engineering, governance, and platform teams. 8. Implement enterprise standards for data security, IAM, compliance (GDPR/HIPAA), observability, and cloud cost optimization. 9. Collaborate with global business stakeholders across the US, India, and Ireland to drive data modernization, cloud migration, and aligned domain strategies. Requirements Required Skills & Experience 15+ years of hands-on experience in data engineering, data platforms, data integration, data governance, and data operations. Strong hands-on experience with Reltio MDM, including configuration, hierarchy management, entity modeling, match/merge, and golden record creation. 5+ years of solid expertise in Databricks Lakehouse (Delta Lake, PySpark, Unity Catalog, MLflow, and Databricks AI). Proven expertise in AWS data ecosystem: S3, Glue, EMR, Lambda, Athena, Redshift, Lake Formation, IAM. Strong experience in implementing Medallion architecture across 100+ data sources using: - Boomi Integrator - Databricks pipelines (batch and streaming) Advanced proficiency in SQL, Python, PySpark, and API-driven integrations. Deep understanding of data governance, metadata, lineage, observability, and MDM frameworks. Experience with modern data stack tools (Snowflake, dbt, Airflow, Kafka) is an advantage. Excellent communication skills, both verbal and written, with the ability to collaborate effectively with teams across the US, India, and Ireland. AWS or Databricks certifications preferred. Education Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field.
