Data Analytics Engineer

The analytics engineer makes sure data is ingested, transformed, scheduled, and ready to be used for analytics. This role is responsible for designing, implementing, and maintaining data pipelines, analytics systems, and reporting solutions. The analytics engineer also models raw data into clean, tested, and reusable datasets to allow business stakeholders to view and understand data in a warehouse or database., * Builds data aggregation pipelines using SQL, .NET, Targit, Power BI

• Creates robust data models and architectures to support analytics initiatives
• Collaborate with business stakeholders to understand their analytics needs and deliver comprehensive reports, dashboards, and models
• Identifies and implements optimizations to continually enhance query performance, reduce processing time, and increase overall productivity
• Designs, develops and maintains data pipelines to ensure efficient and reliable ETL processes
• Implements and maintains analytics systems, data warehouses, or data lakes to store and manage structured and unstructured data
• Creates and maintains dashboards, visualizations, and reports using tools such as Targit & Power BI to enable data-driven decision-making
• Collaborates with Data Analytics Manager to incorporate Power BI and data governance across the organization
• Ensures data quality and accuracy by implementing data validation, monitoring, and error-handling processes

Education: A bachelor’s degree in computer science, data science, software engineering, or related field (R)

Experience: At least five years of experience in data analytics, data engineering, software engineering, or a similar role (R); Expertise in data modeling, ETL development, and data analysis (R)

Skills: Strong ability in SQL for data extraction and manipulation, and proficiency in data warehousing concepts/tools such as Targit, Ignite, and Power BI; Familiarity with cloud-based data platforms such as Azure for data storage and processing; Substantial programming ability using languages/tools such as C++ & .NET for data manipulation and scripting; Solid understanding of relevant data governance, data quality, and data security best practices; Strong problem-solving skills, and the ability to think critically and analytically; Knowledge of ETL processes, data integration, and data warehousing concepts.; Familiarity with data visualization tools such as Targit & Power BI; Knowledge of upgrading data systems like Targit; Excellent communication skills to effectively collaborate with cross-functional teams and present insights to business stakeholders (all R).