Data Engineering Analyst

Role & responsibilities

Job Role: Data Engineering Analyst :

This role is part of a data-focused function responsible for managing and manipulating structured data to build business intelligence tools, conduct analysis, and ensure data quality. The function supports decision-making across the organization by developing models, dashboards, and actionable insights. Increasingly, the role incorporates AI-driven approaches to enhance data processing, predictive analytics, and automation.

Primary Responsibilities:

• Partner with stakeholders to gather requirements and develop BI tools that deliver actionable insights.
• Design and implement data transformation jobs to structure raw data and validate its accuracy and quality.
• Develop dashboards, segmentation models, decision aids, and business case analyses to support strategic initiatives.
• Integrate AI/ML models into data pipelines to enable predictive analytics, anomaly detection, and intelligent automation.
• Collaborate with data scientists to operationalize machine learning models and ensure scalable deployment.
• Work within Agile frameworks, particularly using Rally.
• Identify relevant data sources, metrics, and tools to meet client and stakeholder needs.
• Continuously seek and implement process improvement opportunities using AI-powered tools and techniques.
• Provide solutions to complex data problems and ensure successful implementation.
• Foster collaboration and innovation across cross-functional teams.
• Analyze data, interpret results, and provide expert-level explanations and recommendations.
• Adhere to company policies, employment terms, and directives related to work location, team structure, and flexibility.

Required Qualifications:

• Graduate or Postgraduate degree in Computer Science, Data Engineering, or related field.
• Experience with data visualization tools such as MSBI, Power BI, or Tableau.
• Proficiency in data modeling and ETL tools like Alteryx or MSBI.
• Strong analytical skills with the ability to generate insights from data.
• Hands-on experience with data transformation and data quality assurance.
• Solid SQL skills and familiarity with relational databases.
• Experience working with datasets, dimensions, and metrics.
• Ability to create datasets from diverse sources including Excel, SQL databases, Data Lake, SharePoint.
• Understanding of data governance and approval processes for secure data sharing.
• Familiarity with product lifecycle phases and their impact on data requirements.
• Exposure to AI/ML concepts such as supervised learning, clustering, and NLP.
• Experience with AI tools and platforms (e.g., Azure ML or Python-based ML libraries).
• Ability to interpret and communicate the impact of AI-driven insights to business stakeholders.