01Overview
Position Summary
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
Role & responsibilities
Architecture & Strategy: End-to-end design of scalable, secure, and highly available data architectures leveraging modern cloud data ecosystems (Databricks and Snowflake).
Pipeline Engineering: Architect, optimize, and oversee the deployment of reliable streaming and batch data pipelines (ETL/ELT) to process complex, large-scale datasets.
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.
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.
Real-time Processing: Design and implement robust frameworks for real-time data ingestion and processing to solve business-critical, low-latency use cases.
Hybrid Data Modelling: Harmonize "old school" relational data warehousing patterns (Kimball/Inmon, Star/Snowflake schemas) with unstructured/semi-structured modern paradigms.
Technical Leadership: Act as a core problem-solver for complex data bottlenecks, provide technical governance, and mentor engineering teams on data best practices.
AI Integration: Collaborate with Data Science and AI teams to architect data layers that seamlessly support LLMs, Machine Learning pipelines, and advanced analytics solutions.
Core Experience -
10+ years of progressive experience in Data Engineering, Data Warehousing, and Data Architecture. Demonstrated experience leading architectural decisions for enterprise-scale data platforms.
Preferred candidate profile
Databricks Mastery: Deep hands-on experience with the Databricks Lakehouse platform, Delta Lake, Unity Catalog, and optimizing Spark performance.
Data Pipeline Excellence: Exceptional expertise in designing distributed, fault-tolerant data pipelines using Python, Scala, or SQL.
Snowflake Proficiency: Strong hands-on experience architectural design, performance tuning, and cost-optimization within Snowflake.
Real-time Systems:Proven track record with stream processing technologies (e.g., Structured Streaming, Apache Kafka, Flink, or AWS Kinesis) for real-time use cases.
Polyglot Persistence: Solid foundation in traditional Data Warehousing and relational database management systems (RDBMS). Hands-on experience with NoSQL ecosystems. .
