Principal / Lead Data Architect(AWS Focus)

NEXWARE; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

NEXWARE in Bengaluru seeks a senior data architect to lead end-to-end design of scalable, secure data platforms in the cloud, leveraging Databricks, Snowflake, and AWS.

You will own pipeline engineering, real-time processing, and AI data layer integration, mentor engineers, and ensure robust governance across data products.

Qualifications

• End-to-end design of scalable, secure data architectures.
• Design and optimize data pipelines for large-scale datasets.
• Architect scalable enterprise data components within AWS.

Responsibilities

• Architecture & Strategy: End-to-end design of scalable, secure data architectures using Databricks and Snowflake.
• Pipeline Engineering: Architect and oversee deployment of streaming and batch ETL/ELT pipelines.
• Cloud Architecture: Deploy scalable data platform components in AWS with tight security and IAM.

Skills

Technical leadership

Data architecture

Data pipeline design

Real-time data processing

Data modeling

Tools

Databricks

Snowflake

AWS

APIs (REST/GraphQL)

Job description

Role & responsibilities

• Architecture & Strategy: End-to-end design of scalable, secure, and highly available data architectures leveraging modern cloud data ecosystems (Databricksand 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 APIsand 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.