Technical Specialist

Role summary

Data architecture and analytics engineering

• Design enterprise data architectures for analytics, reporting, and AI/ML; develop conceptual, logical, and physical data models.
• Establish dimensional, semantic, and canonical data models, data standards, governance principles, and best practices; lead architecture reviews.
• Build modular data models with dbt and Snowflake, modernize legacy data assets, and create curated semantic layers.
• Automate data transformations, testing, deployment, and monitoring.

Analysis, governance, and optimization

• Gather analytical requirements and define KPIs; translate business problems into data solutions, insights, and recommendations.
• Support executive dashboards, operational reporting, and performance measurement through advanced analysis of large datasets.
• Define data quality and validation frameworks; establish metadata, cataloging, lineage, and master data management practices.
• Ensure governance, security, privacy, and regulatory compliance; optimize Snowflake storage, compute, queries, loading, and costs.
• Implement role-based and attribute-based access controls; mentor junior colleagues and lead workshops, design discussions, and architecture reviews.

Core platforms and modeling

• Expert-level experience with Snowflake Cloud Data Platform and strong hands-on expertise in dbt Core and dbt Cloud.
• Experience with modern cloud platforms such as AWS, Azure, or GCP.
• Familiarity with orchestration tools such as Airflow, Azure Data Factory, or AWS Glue.
• Modeling expertise includes Kimball dimensional modeling, semantic layers, business metrics, Data Vault, enterprise data warehouses, and logical, physical, canonical, star, and snowflake schemas.

Analytics and engineering skills

• Experience with Power BI, Tableau, Looker, or equivalent BI platforms, KPI frameworks, and executive dashboards.
• Advanced SQL, analytical problem-solving, requirements gathering, and stakeholder management.
• ETL/ELT design, data pipeline development and optimization, data quality validation, and automated testing.
• Knowledge of batch and near-real-time processing, Git branching, CI/CD, automated testing frameworks, and Agile or DevOps methodologies.
• Infrastructure-as-Code knowledge is a plus.