We are seeking a highly seasoned Lead/Principal Data Architect with over a decade of experience to design, build, and scale our next-generation data platform. In this role, you will be the mastermind behind our data strategy, bridging the gap between complex business requirements and robust technical execution. You will bring exceptional problem-solving abilities, deep expertise in Databricks and Snowflake, and a proven track record of engineering high-throughput, real-time data pipelines that drive business value at scale. Key Information • Location: Bengaluru • Project Duration: Ongoing • Shift Timings: UK Hours • Experience Required: 10+ Years Core Responsibilities Architecture & Strategy Design end-to-end scalable, secure, and highly available data architectures leveraging modern cloud data ecosystems (Databricks and Snowflake). Establish data governance frameworks and strategic direction for enterprise data platforms. Pipeline Engineering Architect, optimize, and oversee the deployment of reliable streaming and batch data pipelines (ETL/ELT) to process complex, large-scale datasets. Ensure fault-tolerance, performance optimization, and cost-efficiency across all pipeline implementations. Cloud Architecture Architect and deploy scalable enterprise data platform components natively within the AWS ecosystem, ensuring tight integration with core security, IAM, and networking protocols. Design cloud-native solutions that maximize performance and minimize operational overhead. API Ingestion & Orchestration Design and implement robust data ingestion frameworks leveraging Databricks APIs and external REST/GraphQL APIs for automated workflows, platform orchestration, and data delivery. Build scalable ingestion solutions that support diverse data sources and formats. Real-time Processing Design and implement robust frameworks for real-time data ingestion and processing to solve business-critical, low-latency use cases. Architect streaming solutions that deliver insights with minimal latency while maintaining data quality and reliability. Hybrid Data Modelling Harmonize traditional relational data warehousing patterns (Kimball/Inmon, Star/Snowflake schemas) with unstructured and semi-structured modern paradigms. Create flexible, scalable data models that support diverse analytical and operational use cases. Technical Leadership Act as a core problem-solver for complex data bottlenecks and performance challenges. Provide technical governance, establish best practices, and mentor engineering teams on data architecture and optimization strategies. Drive technical excellence across the organization. AI Integration Collaborate with Data Science and AI teams to architect data layers that seamlessly support LLMs, Machine Learning pipelines, and advanced analytics solutions. Design feature stores and data infrastructure optimized for AI/ML workloads. Requirements Required Qualifications Experience • 10+ years of progressive experience in Data Engineering, Data Warehousing, and Data Architecture • Proven track record of designing and implementing enterprise-scale data platforms • Demonstrated experience leading technical teams and influencing architectural decisions Educational Background • Bachelor of Engineering (BE) / B.Tech in Computer Science or related field • OR Master of Computer Applications (MCA) / M.Tech Mandatory Technical Skills • Databricks: Deep hands-on expertise with Databricks Lakehouse platform, Delta Lake, Unity Catalog, and Spark performance optimization • Snowflake: Strong experience in architectural design, performance tuning, query optimization, and cost-optimization strategies • Python: Advanced proficiency for data pipeline development and scripting • Scala: Solid experience for Spark-based distributed computing • SQL: Expert-level SQL skills for complex query optimization and data modeling • Structured Streaming: Hands-on experience with Apache Spark Structured Streaming for real-time data processing • Apache Kafka: Proven expertise in designing and implementing Kafka-based data streaming architectures • Flink/AWS Kinesis: Experience with Apache Flink or AWS Kinesis for stream processing and real-time analytics Technical Expertise • Data Pipeline Excellence: Exceptional expertise in designing distributed, fault-tolerant data pipelines using Python, Scala, or SQL • Real-time Systems: Proven track record with stream processing technologies for real-time, low-latency use cases • Polyglot Persistence: Solid foundation in traditional Data Warehousing and relational database management systems (RDBMS); hands-on experience with NoSQL ecosystems • Cloud Platforms: Deep understanding of AWS services including Lambda, S3, EC2, IAM, and VPC configurations • Data Governance: Experience implementing data governance, lineage tracking, and metadata management solutions Soft Skills & Competencies • Problem-Solving: Elite analytical mindset with a proven track record of troubleshooting complex distributed systems and resolving performance degradation issues • Communication: Ability to articulate complex technical architectures clearly to both engineering teams and non-technical business stakeholders • Leadership: Natural ability to mentor, guide, and elevate technical teams; strong influence without authority • Collaboration: Proven ability to work cross-functionally with data scientists, engineers, and business stakeholders • Attention to Detail: Meticulous approach to system design, documentation, and quality assurance Preferred / Good-to-Have Qualifications • AI/ML Data Readiness: Exposure to architecting data solutions tailored for AI, such as vector databases (e.g., Pinecone, Milvus), feature stores, or building data pipelines for generative AI/LLM applications • Certifications: Databricks Certified Data Architect, Snowflake Certified Advanced Architect, or AWS Solutions Architect certifications • Experience with data quality frameworks and tools (e.g., Great Expectations, dbt) • Knowledge of containerization technologies (Docker, Kubernetes) and CI/CD pipelines • Experience with data cataloging and metadata management platforms • Exposure to graph databases and advanced NoSQL technologies • Experience with cost optimization strategies for cloud data platforms