Senior Data Architect

About the Role:

We are looking for a Data Architect with a strong background in data engineering & cloud data platforms. The ideal candidate will design and implement scalable data architectures that power enterprise analytics, AI/ML, and GenAI solutions ensuring data availability, quality, and governance across the organization.

Key Responsibilities:

Data Architecture & Strategy

• Design & Architecture: Design and implement robust, scalable, and optimized data engineering solutions on the Databricks platform. Architect data pipelines that scale efficiently and reliably.
• Data Pipeline Development: Develop ETL/ELT pipelines leveraging Databricks notebooks, Delta Lake, Snowflake tech stack, Azure Data Factory etc.
• Cloud Integration: Work closely with cloud platforms like Azure, AWS, or GCP to integrate Databricks or Snowflake with data storage (e.g., ADLS, S3, etc.), databases, and other services.
• Performance Optimization: Optimize the performance of data workflows by tuning Databricks clusters, improving query performance, and identifying bottlenecks in data processing.
• Collaboration: Collaborate with data scientists, analysts, and business stakeholders to understand business requirements and translate them into scalable data solutions.
• Data Governance & Security: Ensure best practices for data security, governance, and compliance when working with sensitive or large datasets.
• Automation & Monitoring: Automate data pipeline deployments and create monitoring dashboards for ongoing performance checks.
• Continuous Improvement: Stay up to date with the latest Databricks features and Snowflake eco system best practices to continuously improve existing systems and processes.

Required Skills & Experience:

• 12+ years of experience in Data Architecture / Data Engineering roles.
• Proven expertise in data modeling, ETL/ELT design, and cloud-based data solutions (AWS Redshift, Snowflake, BigQuery, or Synapse).
• Hands-on experience with data pipeline orchestration tools (Airflow, DBT, Azure Data Factory, etc.).
• Proficiency in Python, SQL, and Spark for data processing and integration.
• Experience with API integrations and data APIs for AI systems.
• Excellent communication and stakeholder management skills.