Data Architect

Job description

The ideal candidate will be responsible for designing, implementing, and governing scalable data platforms that support analytics, reporting, AI/ML, and business intelligence initiatives. This role requires deep technical expertise in Databricks, data modeling, cloud data engineering, and architecture best practices. Qualifications Experience: 10 to 14 Years Work location: Hyderabad Required Skills • Strong experience in Databricks Lakehouse Platform. • Expertise in Apache Spark (Pyspark/Scala Spark). • Hands-on experience with Delta Lake, Unity Catalog, Delta Live Tables, and Databricks Workflows. • Strong understanding of data warehousing and dimensional modeling concepts. • Experience with cloud platforms: Microsoft Azure (preferred), AWS, GCP. • Knowledge of Azure Data Factory (ADF), Azure Synapse Analytics, Azure Data Lake Storage (ADLS), Kafka/Event Hubs. • Expertise in SQL and performance tuning. • Experience designing ETL/ELT frameworks and data integration solutions. • Understanding of data governance, security, access controls, and compliance frameworks. • Experience with DevOps, CI/CD, Infrastructure as Code (Terraform preferred). • Exposure to BI tools such as Power BI, Tableau, or Looker. Preferred Skills • Enterprise Data Architecture. • Lakehouse Architecture. • Data Mesh / Data Fabric concepts. • Master Data Management (MDM). • Metadata Management. • Data Governance. • Real-time Analytics Architecture. • AI/ML Data Platform Design. • Experience with Generative AI and LLM-based data solutions. • Knowledge of Microsoft Fabric. • Experience with Snowflake, Big Query, or Redshift. • Exposure to MLOps frameworks and AI governance. • Experience in regulated industries such as Banking, Healthcare, Insurance, or Retail. Responsibilities • Define and implement enterprise-wide data architecture strategies aligned with business objectives. • Design scalable and secure data platforms using the Databricks Lakehouse architecture. • Architect batch and real-time data ingestion frameworks from multiple source systems. • Develop data models, data warehouses, data lakes, and Lakehouse solutions. • Lead the migration of legacy data platforms to cloud-based modern data architectures. • Define data governance, metadata management, data quality, lineage, and security standards. • Collaborate with business stakeholders, data engineers, analysts, and data scientists to understand requirements and translate them into technical solutions. • Establish architecture frameworks, best practices, and reusable design patterns. • Optimize data pipelines and platform performance for scalability and cost efficiency. • Support advanced analytics, AI/ML, and GenAI use cases through robust data architecture. • Conduct architecture reviews and provide technical guidance to engineering teams. • Ensure compliance with enterprise security and regulatory requirements.