Offshore Data Architect

Offshore Data Architect Dataiku, Snowflake & ETL

Experience

10 15 years overall experience in Data & Analytics
5+ years in Data Architecture and Solution Design

Loc : PAN India

Immediate to 30Days joiners

Technical Skills

• Dataiku DSS (expert level)
• Snowflake (architecture & hands-on implementation)
• ETL / ELT Design & Development
• Advanced SQL
• Cloud platforms: AWS / Azure / GCP (at least one)
• Cloud storage: S3 / ADLS / GCS
• Data modeling techniques (dimensional, normalized, data vault preferred)
• Workflow orchestration concepts

Data Architecture & Design

• Design end-to-end data architectures including ingestion, transformation, storage, analytics, and consumption layers.
• Define logical, physical, and canonical data models aligned with business requirements.
• Architect scalable and secure cloud-based data platforms using Snowflake and related services.
• Establish data standards, best practices, and architectural guidelines for offshore delivery teams.
• Lead the design and implementation of Dataiku DSS projects for data preparation, feature engineering, analytics, and ML workflows.
• Design reusable Dataiku recipes, flows, and automation pipelines.
• Integrate Dataiku with Snowflake, cloud storage, APIs, and third-party data sources.
• Implement Dataiku governance, versioning, collaboration, and deployment strategies.
• Optimize Dataiku performance and resource utilization.

Snowflake Architecture & Engineering

• Architect and implement Snowflake data warehouses, including databases, schemas, warehouses, and roles.
• Design optimized Snowflake data models (star, snowflake, and hybrid).
• Implement Snowflake features such as:
  • Streams & Tasks
  • Time Travel & Zero Copy Cloning
  • Secure Data Sharing
  • Snowflake SQL optimization
• Ensure performance tuning, cost optimization, and workload isolation.
• Define Snowflake security, access controls, and data masking policies.

ETL / Data Integration

• Architect and implement ETL/ELT pipelines using Dataiku and other ETL tools.
• Design ingestion frameworks for:
  • Batch data
  • Near real-time / streaming (where applicable)
  • API and file-based integrations
• Handle complex data transformations, validations, and reconciliations.
• Ensure data quality, lineage, and auditability across pipelines.

Good to have :

• Define data governance frameworks, including metadata management and lineage.
• Implement data quality checks, validation rules, and monitoring.
• Ensure compliance with security and regulatory standards (PII, GDPR, etc.).
• Support audit and compliance requirements.