Role Overview

As an Intermediate Analytics Engineer, you will design, implement, and optimize analytics data models and pipelines. You’ll be responsible for developing robust, scalable solutions that enable high-quality business intelligence and analytics.

Key Responsibilities

• Design and implement relational, multi-dimensional, and domain-specific semantic models. Apply data normalization principles.
• Develop and optimize analytics pipelines using dbt, SQLMesh, and similar tools.
• Implement and monitor data quality frameworks (dbt tests, SodaCL) to ensure reliable datasets.
• Integrate and transform data from diverse sources for analytics consumption.
• Write efficient SQL and Python code for data transformation and analysis.
• Develop dashboards and reports in Power BI and Excel.
• Work with data analysts, engineers, and business stakeholders to gather requirements and deliver solutions.
• Maintain comprehensive documentation of data models, pipelines, and processes.
• Use Git and Azure DevOps/GitHub for version control, pull requests, and CI/CD pipeline management.
• Apply knowledge of open table formats (Delta, Hudi, Iceberg), open lakehouse concepts, open catalogs (Unity Catalog), and data sharing protocols (Redshift Share, Delta Sharing).
• Understand and contribute to data mesh concepts and goals.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, or related field.
• Proven experience in analytics engineering or data analytics.
• Strong proficiency in SQL, Python, and data modeling.
• Experience with dbt, SQLMesh, and data quality frameworks.
• Familiarity with BI tools (Power BI, Excel).
• Experience with version control (Git) and CI/CD pipelines.
• Understanding of open table formats, lakehouse concepts, and data mesh principles.
• Strong analytical, problem-solving, and communication skills.

Preferred Skills

• Certification in analytics engineering or cloud technologies.
• Experience with cloud platforms (Azure, AWS, Google Cloud).
• Knowledge of software engineering best practices.