Principal Data Architect

About Saarthee:

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. Thats why we call ourselves Saartheeinspired 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.

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 solutionspaired 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:

- Bachelors degree in technology (B.Tech) or equivalent technical qualification.

- 8+ years of experience across Data Engineering, Data Warehousing, and Data Architecture.

- Proven experience driving architectural decisions for enterprise-scale data platforms.

- 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.

Skills

Data Engineering, Data Management, Data Architect, AWS, Python, Data Warehousing, SQL, Scala