We are seeking a highly skilled Senior Data Architect with extensive experience in designing, implementing, and optimizing enterprise-grade data platforms. The ideal candidate will be responsible for defining end-to-end data architecture, enabling scalable data pipelines, modernizing legacy systems, and implementing robust data governance and DataOps practices. This role requires deep technical hands-on capability in Snowflake, ELT/ETL engineering, data modeling, and cloud-based data ecosystems.

You will collaborate closely with engineering, analytics, cloud, and product teams to architect and deliver secure, high-performance data solutions that support mission-critical business operations.

Core Responsibilities :

Data Architecture & Solution Design :

- Architect and evolve enterprise data platforms leveraging Snowflake, cloud-native storage, and distributed data processing frameworks.

- Define reference architecture, data ingestion frameworks, canonical models, and standards for scalable data pipelines.

- Lead design of multi-zone data lake and data warehouse architectures supporting batch, streaming, and CDC pipelines.

- Develop high-performing schemas including star/snowflake models, data vault, and domain-driven designs.

- Drive architectural decisions related to partitioning, clustering, micro-partition optimization, query tuning, and cost governance within Snowflake.

Data Engineering & Pipeline Development :

- Oversee and guide technical teams in building complex ETL/ELT pipelines using Python, Java, or Scala.

- Lead modernization of legacy systems and architect large-scale data migration solutions.

- Define ingestion patterns (real-time, near real-time, batch) and implement scalable, reusable data pipelines.

- Ensure high-quality code delivery aligned with CI/CD pipelines, version control, and automated testing frameworks.

DBT & Data Transformation Frameworks :

- Implement and scale DBT for transformation orchestration, data quality tests, documentation, lineage tracking, and semantic modeling.

- Establish best practices around modularity, model reuse, macros, materializations, and environment management in DBT.

Data Quality, Governance, and Security :

- Define and enforce standards for metadata management, data cataloging, lineage, and data lifecycle policies.

- Implement data governance frameworks including access control, encryption, masking, and compliance requirements.

- Collaborate with InfoSec teams to design secure data zones, identity access management, and audit mechanisms across cloud platforms.

Cloud & Platform Engineering :

- Drive cloud-native data architecture initiatives using Snowflake, AWS/Azure/GCP services, and containerized workloads.

- Optimize platform costs, storage strategies, and compute utilization across cloud environments.

- Integrate Snowflake with enterprise data platforms, APIs, Kafka streams, SAP systems, and downstream analytics tools.

Stakeholder Management & Technical Leadership :

- Act as the primary technical architect for large data programs and ensure architectural integrity across all phases.

- Guide engineering teams through code reviews, solution reviews, and architecture compliance checks.

- Collaborate with business, data science, and BI teams to shape data solutions supporting analytics and decision-making.

Required Skills & Qualifications :

- Minimum of 10+ years in data engineering, data architecture, and enterprise data platform development.

- Deep hands-on expertise in Snowflake architecture, query engineering, performance tuning, security models, and operational best practices.

- Strong experience with ETL/ELT frameworks, large data migrations, and high-volume data processing systems.

- Proficiency in Python / Java / Scala, advanced SQL, and data integration techniques.

- Expertise in data modeling, dimensional modeling, schema design, and optimization strategies.

- Strong understanding of cloud data warehousing, distributed computing, data lakes, and cloud-native storage.

- Experience implementing and managing DBT in large-scale environments.

- Strong understanding of data governance, MDM, metadata management, and DataOps practices.