Role Overview
The Data Engineer will collect, process, analyze, and interpret large volumes of structured and unstructured data to support business intelligence, fraud analytics, operational reporting, and data-driven decision-making.
What You Will Do
The Data Engineer will develop and maintain Business Intelligence (BI) dashboards, analytical reports, KPI monitoring solutions, and interactive data visualizations to provide actionable business insights.
Why It Might Be a Fit
The ideal candidate will have strong hands-on experience in Data Analytics, Business Intelligence, Fraud Analytics, Dashboard Development, and Microsoft-based Data Technologies.
Requirements
- 3–4 years of relevant experience for the 2 mid-level openings, or 5–8 years for the senior opening, across Data Analytics, Business Intelligence, Data Science, Data Engineering, or related disciplines.
- Strong hands-on experience in Data Analytics, Business Intelligence, Fraud Analytics, Dashboard Development, and Microsoft-based Data Technologies.
- Practical experience with Microsoft Power BI, including Power Query, DAX, data modelling, dashboard development, and report publishing.
- Strong proficiency in SQL, including data extraction, complex queries, joins, aggregations, data transformation, and query optimization.
- Experience with Microsoft SQL Server, Azure SQL, Microsoft Fabric, Azure Synapse Analytics, or similar Microsoft data technologies is advantageous.
- Knowledge of Fraud Analytics, Fraud Detection, Anomaly Detection, Risk Analytics, and Suspicious Pattern Identification.
- Understanding of Data Science concepts, Statistical Analysis, Predictive Modelling, Exploratory Data Analysis, and Data Visualization.
- Experience using Python, particularly Pandas, NumPy, Scikit-learn, or similar data analysis libraries, is advantageous.
- Familiarity with ETL/ELT processes, Data Pipelines, Data Integration, Data Warehousing, and Data Transformation.
- Hands-on experience with Azure Data Factory, SQL Server Integration Services (SSIS), Microsoft Fabric Data Pipelines, or equivalent data engineering tools is advantageous.
- Good understanding of Data Modelling, Relational Databases, Data Quality, Data Governance, and Reporting Architecture.
- Experience integrating data from multiple sources, including relational databases, APIs, Excel files, enterprise applications, and cloud-based platforms.
- Ability to develop and maintain automated reporting solutions, analytical workflows, and reusable datasets.
- Knowledge of Machine Learning, Predictive Analytics, Classification Models, and Anomaly Detection Algorithms is advantageous.
- Experience working with large datasets, identifying data trends, resolving inconsistencies, and translating findings into business recommendations.
- Utilities, Energy, Electricity, Water, Telecommunications, or similar industry experience is advantageous, particularly exposure to billing analytics, consumption analysis, revenue assurance, or fraud detection.
- Experience supporting consulting engagements, enterprise analytics projects, or client-facing data initiatives is beneficial.
Benefits
- Collaborative and dynamic work environment
- Flexible work arrangements
To apply for this job please visit linkedin.com.
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