Principal Data Architect

Saarthee; Bengaluru; Hybrid; INR 4,800,000 - 7,000,000; Full time

Job summary

Saarthee in Bengaluru is seeking a Lead/Principal Data Architect with 8+ years to own end-to-end data strategy for a next-generation platform. The role emphasizes Databricks and Snowflake, real-time data pipelines, lakehouse, NoSQL, and AI-driven data solutions, with hybrid work mode and collaboration across data engineering, data science, and security teams.

You will lead architecture decisions, mentor engineers, and ensure scalable, secure data delivery.

Qualifications

• Bachelor’s degree in technology or equivalent technical qualification.
• 8+ years of experience across Data Engineering, Data Warehousing, and Data Architecture.
• Strong experience in designing scalable, reliable, and high-performance data architectures.
• Extensive hands‑on experience with Databricks Lakehouse Platform; Strong expertise in Delta Lake, Unity Catalog, and Apache Spark.
• Strong expertise in designing distributed, scalable, and fault‑tolerant data pipelines.
• Proficiency in Python, Scala, and/or SQL. Experience with both batch and real‑time data processing.
• Hands‑on expertise in Snowflake architecture and solution design. Experience with performance optimization, workload management, and cost optimization.
• Experience with real‑time and near‑real‑time data processing. Hands‑on experience with Kafka, Spark Structured Streaming, Flink, and/or AWS Kinesis.
• Strong understanding of data warehousing, RDBMS, dimensional modeling, and data architecture. Practical experience with NoSQL databases and distributed data stores.
• Strong analytical and problem‑solving skills with experience resolving complex distributed‑system and performance issues.
• Ability to identify architectural bottlenecks and recommend scalable, reliable, and cost‑effective solutions.
• Excellent communication skills with the ability to explain complex technical concepts to technical and business stakeholders.

Responsibilities

• Architecture & Strategy: Design end-to-end data architectures that are scalable, secure, and highly available, built on modern cloud ecosystems such as Databricks and Snowflake.
• Pipeline Engineering: Build, tune, and manage the rollout of dependable streaming and batch (ETL/ELT) pipelines capable of handling large, complex datasets.
• Cloud Architecture: Design and implement enterprise-grade data platform components natively on AWS, with close integration into core security, IAM, and networking layers.
• API Ingestion & Orchestration: Develop strong data ingestion frameworks using Databricks APIs and external REST/GraphQL APIs to power automated workflows, orchestration, and data delivery.
• Real-time Processing: Build resilient real-time ingestion and processing frameworks to address critical, low-latency business needs.
• Hybrid Data Modelling: Blend classic relational data warehousing approaches with modern unstructured and semi-structured data paradigms.
• Technical Leadership: Serve as the go-to expert for resolving complex data challenges, set technical governance standards, and mentor engineering teams on best practices.
• AI Integration: Partner with Data Science and AI teams to design data layers that seamlessly support LLMs, machine learning pipelines, and advanced analytics.

Skills

Leadership

Communication skills

Problem-solving

Strategic thinking

Education

Bachelor’s degree in Technology

Tools

Databricks Lakehouse Platform

Delta Lake

Unity Catalog

Apache Spark

Snowflake

Kafka

Spark Structured Streaming

Flink

AWS Kinesis

Python

Scala

SQL

REST/GraphQL APIs

Job description

Saarthee is a Global Strategy, Analytics, Technology and AI consulting company, where our passion for helping others fuels our approach and our products and solutions. Our diverse and global team work with one objective in mind: Our Customers’ Success. At Saarthee, we are passionate about guiding organizations towards insights fueled success. That’s why we call ourselves Saarthee–inspired by the Sanskrit word ‘Saarthi’, which means charioteer, trusted guide, or companion. Cofounded in 2015 by Mrinal Prasad and Shikha Miglani, Saarthee already encompasses all the components of Data Analytics consulting. Saarthee is based out of Philadelphia, USA with offices in UK and India.

Work Mode: Hybrid

Min-Max Experience: 8-10 years

Position Summary:

This role calls for a Lead/Principal Data Architect with 8+ years of experience to own the end-to-end data strategy for a next-generation platform, translating business requirements into scalable technical solutions. The ideal candidate combines deep expertise in Databricks and Snowflake with a proven track record building high-throughput, real-time data pipelines, while bridging traditional data warehousing discipline with modern Lakehouse architecture, NoSQL systems, and AI-driven data solutions — paired with strong problem-solving skills and the ability to connect business needs with technical execution.

Your Role Responsibilities and Duties:

• Architecture & Strategy: Design end-to-end data architectures that are scalable, secure, and highly available, built on modern cloud ecosystems such as Databricks and Snowflake.
• Pipeline Engineering: Build, tune, and manage the rollout of dependable streaming and batch (ETL/ELT) pipelines capable of handling large, complex datasets.
• Cloud Architecture: Design and implement enterprise-grade data platform components natively on AWS, with close integration into core security, IAM, and networking layers.
• API Ingestion & Orchestration: Develop strong data ingestion frameworks using Databricks APIs and external REST/GraphQL APIs to power automated workflows, orchestration, and data delivery.
• Real-time Processing: Build resilient real-time ingestion and processing frameworks to address critical, low-latency business needs.
• Hybrid Data Modelling: Blend classic relational data warehousing approaches (Kimball/Inmon, Star/Snowflake schemas) with modern unstructured and semi-structured data paradigms.
• Technical Leadership: Serve as the go-to expert for resolving complex data challenges, set technical governance standards, and mentor engineering teams on best practices.
• AI Integration: Partner with Data Science and AI teams to design data layers that seamlessly support LLMs, machine learning pipelines, and advanced analytics.

Required Skills and Qualifications:

• Bachelor’s degree in technology (B.Tech) or equivalent technical qualification.
• 8+ years of experience across Data Engineering, Data Warehousing, and Data Architecture.
• Strong experience in designing scalable, reliable, and high-performance data architectures.
• Extensive hands‑on experience with Databricks Lakehouse Platform; Strong expertise in Delta Lake, Unity Catalog, and Apache Spark. Experience with Spark performance tuning and optimization.
• Strong expertise in designing distributed, scalable, and fault‑tolerant data pipelines.
• Proficiency in Python, Scala, and/or SQL.Experience with both batch and real‑time data processing.
• Hands‑on expertise in Snowflake architecture and solution design. Experience with performance optimization, workload management, and cost optimization.Strong understanding of enterprise‑scale Snowflake implementations.
• Experience with real‑time and near‑real‑time data processing. Hands‑on experience with Kafka, Spark Structured Streaming, Flink, and/or AWS Kinesis.Understanding of event‑driven and low‑latency data architectures.
• Strong understanding of data warehousing, RDBMS, dimensional modeling, and data architecture. Practical experience with NoSQL databases and distributed data stores. Ability to select appropriate technologies based on scalability, performance, and cost.
• Strong analytical and problem‑solving skills with experience resolving complex distributed‑system and performance issues.
• Ability to identify architectural bottlenecks and recommend scalable, reliable, and cost‑effective solutions.
• Excellent communication skills with the ability to explain complex technical concepts to technical and business stakeholders.

Good-to-Have Skills

• Experience designing AI/ML-ready data platforms and architectures.
• Familiarity with vector databases such as Pinecone or Milvus.
• Exposure to feature stores and data pipelines supporting GenAI/LLM applications.
• Relevant certifications such as Databricks Certified Data Architect or Snowflake Certified Advanced Architect.