Design, implement, and optimize enterprise-grade data warehouse systems with a focus on Kimball dimensional modeling to support analytical and operational requirements.
Build business-friendly dimensional models (facts, dimensions, star schemas, snowflake schemas) to enable efficient reporting, self-service analytics, and scalable growth.
Lead the development, monitoring, and optimization of ETL/ELT pipelines, ensuring high-quality, accurate, and timely data ingestion.
Write, refine, and optimize SQL queries for performance and reliability across high-volume datasets.
Partner with Data Analysts, Data Scientists, and Business stakeholders to translate business needs into robust data solutions while ensuring data consistency, security, and governance.
Establish and maintain best practices, documentation, and standards for data modeling, pipelines, and workflows.
Guide and mentor junior engineers, fostering a culture of technical excellence and continuous learning within the team
Requirements
5+ years of hands-on experience in data engineering, with at least 2+ years focusing on data warehousing and dimensional modeling. Previous leadership or mentoring experience is preferred.
Strong command of SQL (complex queries, performance tuning).
Proficiency in Kimball dimensional modeling methodologies.
Experience with ETL/ELT development (e.g., Airflow, dbt, Spark, or similar).
Programming experience in Python or other languages for data workflows.
Familiarity with cloud-based data platforms (e.g., BigQuery, Snowflake, Redshift) and distributed systems.
Ability to troubleshoot complex data challenges, design scalable solutions, and ensure performance efficiency.
Strong ability to partner with technical and non-technical stakeholders, explain data concepts clearly, and align solutions with business goals
