What You Will Do
· Design, develop, and maintain BI and data solutions using SQL Server technologies and Power BI
· Write, debug, and optimize complex SQL queries to support reporting and analytics needs
· Develop and support ETL processes using SQL Server Integration Services (SSIS)
· Build and maintain semantic models using SQL Server Analysis Services (SSAS)
· Create and enhance dashboards and reports using SQL Server Reporting Services (SSRS) and Power BI
· Develop and maintain data design artifacts such as source‑to‑target mappings, data flow diagrams, and RCA documents
· Partner with stakeholders to understand business requirements and translate them into scalable data solutions
· Participate in Agile ceremonies and contribute to continuous improvement of data engineering practices
·
Ensure data quality, performance, and reliability across
BI
solutions
Who You Are (Basic Qualifications)
· Bachelor’s degree in Computer Science, Engineering, or a related field or equivalent practical experience
· At least 3 years of hands‑on experience in BI , data engineering, or analytics roles
· Strong proficiency in SQL, including writing complex queries and performance optimization
· Experience working with SSIS, SSAS, SSRS, and Power BI
· Solid understanding of BI concepts, including OLTP vs. OLAP architectures
· Experience working in an Agile development environment
· Strong problem‑solving skills with the ability to work independently
· Effective written and verbal communication skills
What Will Put You Ahead
· Hands‑on experience with Python for data processing and analytics
· Exposure to cloud and data platforms such as AWS (e.g., S3, Lambda, Glue, EC2, IAM) and/or Databricks (e.g., Spark, Delta Lake, notebooks, workflows)
· Experience designing and deploying data solutions using Python and modern data frameworks
· Experience executing AI or ML experiments that improved productivity or automation
· Proven ability to deliver measurable business impact through AI‑driven or data‑driven solutions
· Exposure to MLOps/LLMOps practices, including experiment tracking, model deployment, monitoring, and responsible AI considerations
Apply through whichever channel suits you best.