Job Title :

Principal / Lead Data Architect (AWS Focus)

Locations : Bengaluru

Minimum Experience : 10 Years

Mandatory Skills :

- Databricks, Python, Scala, SQL, Snowflake, Structured Streaming, Apache Kafka, Flink/AWS Kinesis

Skill to Evaluate :

- Databricks, Python, Scala, SQL, Snowflake, Structured Streaming, Apache Kafka, Flink/AWS Kinesis

Education Qualification :

B. Tech/MTech

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. 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. The ideal candidate possesses a rare blend of deep respect for traditional data warehousing alongside a forward-thinking mastery of modern Lakehouse architectures, NoSQL systems, and AI-driven data solutions.

Key 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.

Required Qualifications & Skills:

- Qualification: Bachelor of Technology (B.Tech).

- 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.

- Technical Expertise:

1. Databricks Mastery: Deep hands-on experience with the Databricks Lakehouse platform, Delta Lake, Unity Catalog, and optimizing Spark performance.

2. Data Pipeline Excellence: Exceptional expertise in designing distributed, fault-tolerant data pipelines using Python, Scala, or SQL.

3. Snowflake Proficiency: Strong hands-on experience architectural design, performance tuning, and cost-optimization within Snowflake.

4. 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.

5. Polyglot Persistence: Solid foundation in traditional Data Warehousing and relational database management systems (RDBMS). Hands-on experience with NoSQL ecosystems.

- Soft Skills & Problem Solving:

1. Elite Problem-Solving: A strong analytical mindset with a track record of troubleshooting complex distributed systems and performance degradation issues.

2. Communication: Ability to articulate complex technical architectures clearly to both engineering teams and non-technical business stakeholders.

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.

- Relevant certifications: (e.g., Databricks Certified Data Architect, Snowflake Certified Advanced Architect).