Data Architect - Snowflake DB

Role Overview:

We are looking for an experienced Data Architect with strong expertise in Snowflake, modern cloud data platforms, and Business Intelligence (BI). The ideal candidate will own end-to-end data architecture, design scalable data engineering and analytics solutions, and translate complex business requirements into reliable, high-performance data platforms. The role requires a combination of hands-on technical architecture, BI and semantic modelling expertise, stakeholder management, and technical leadership.

Key Responsibilities:

1. Data Architecture & Platform Design:

- Define end-to-end enterprise data architecture, including modern data warehouses, data lakes, and Lakehouse platforms.

- Design scalable, secure, and cost-effective data solutions using Snowflake and/or Databricks.

- Establish data modeling standards, architecture principles, integration patterns, and technology best practices.

- Design data platforms using Medallion Architecture (Bronze, Silver, Gold), dimensional modelling, and enterprise data models.

- Evaluate technology choices and recommend suitable architectural solutions based on business and technical requirements.

2. Snowflake & Data Engineering:

- Architect data ingestion, transformation, and processing pipelines using SQL, Python, Spark, and relevant cloud-native services.

- Design ELT/ETL frameworks and orchestrate data workflows across multiple enterprise systems.

- Optimize query performance, compute utilization, storage, scalability, and platform costs.

- Define approaches for batch and real-time/streaming data processing where required.

- Establish engineering standards for reusable pipelines, testing, monitoring, CI/CD, and deployment.

3. Business Intelligence & Analytics:

- Design enterprise BI architecture, reporting frameworks, and self-service analytics solutions.

- Strong expertise in at least one BI platform, such as Power BI, Tableau, or Qlik.

- Define semantic models, dimensional models, metrics, KPIs, and business calculation standards.

- Establish governed datasets, reusable business measures, and consistent reporting definitions across business functions.

- Optimize BI performance, dashboard responsiveness, data refresh, and large-scale reporting workloads.

- Partner with business stakeholders to translate analytical requirements into actionable insights and reporting solutions.

4. Data Governance, Quality & Security:

- Define data governance, data quality, metadata management, lineage, and master/reference data standards.

- Implement appropriate data access controls, role-based security, masking, encryption, and compliance controls.

- Establish data validation and reconciliation frameworks to ensure accuracy, consistency, and reliability.

- Promote trusted, discoverable, and reusable enterprise data assets.

5. Technical Leadership & Stakeholder Management:

- Lead architecture discussions, technical design reviews, and solution walkthroughs.

- Work closely with business leaders, product owners, data engineers, BI developers, and cloud teams.

- Mentor engineering teams and establish coding, modeling, documentation, and architecture best practices.

- Support presales activities, solution proposals, effort estimation, and technical presentations where required.

- Evaluate emerging technologies and identify opportunities to integrate AI/GenAI into data and analytics platforms.

Required Skills & Experience:

- 10 - 15+ years of experience in data engineering, data architecture, data warehousing, and analytics.

- Strong hands-on architecture expertise in Snowflake.

- Excellent SQL skills and experience with Python, PySpark, or equivalent data processing technologies.

- Strong understanding of cloud platforms such as Azure, AWS, or GCP.

- Expertise in data modeling, dimensional modeling, data warehousing, lakehouse architecture, and ETL/ETL patterns.

- Strong BI expertise in Power BI, Tableau, or Qlik, including semantic modeling, KPI design, and performance optimization.

- Experience designing and implementing enterprise-scale data platforms and integrating data from multiple source systems.

- Knowledge of data governance, security, data quality, lineage, and CI/CD practices.

- Strong communication, analytical, problem-solving, and stakeholder management skills.

Preferred Qualifications:

- Experience with DBT, Databricks Unity Catalog, Snowflake governance features, or comparable data management capabilities.

- Knowledge of Microsoft Fabric, Azure Data Factory, AWS Glue, or similar data integration services.

- Exposure to streaming technologies such as Kafka or cloud-native streaming services.

- Experience with AI/ML platforms, GenAI-enabled analytics, or natural-language interfaces for BI.

- Relevant Snowflake, Databricks, cloud, or BI certifications.