Senior Data Analyst - Bryanston at Experian

Qualifications

• BSc/BA in Computer Science math, software engineering, statistics, data science or a related technical field with 3 to 5 years of work experience. Microsoft SQL certification.

Functional

• Proven experience as a Data Scientist or Data Analyst.
• Experience in data warehousing, ETL processes, data mining and data modelling.
• Experience with big data technologies (e.g., Hadoop, Spark).
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of Microsoft SQL Server, T-SQL, Azure and Python.
• Proficiency in data visualization tools (e.g., Power BI, Tableau).
• Analytical mind and business acumen.
• Strong math skills (e.g., statistics, algorithms).
• Problem-solving aptitude.
• Excellent communication and presentation skills.
• Ability to work independently and with team members from different backgrounds.
• Excellent attention to detail.
• A drive to learn and master new technologies and techniques.
• Working with large datasets and moving of files between different environments.
• Import and Export data from different database technologies.
• Process automation and monitoring.
• Code migration from legacy languages and systems.
• Data Cleansing
• Data profiling
• Excellent team player and collaboration skills.
• Ability to prioritize and manage multiple tasks and projects simultaneously.

Technical

Experienced and knowledgeable in the use of tools and techniques such as

• Microsoft SQL Server
• T-SQL
• Azure Data Platform Stack
• Azure Data Lakes/Blob
• Azure HDInsight (Apache Spark)
• Azure Databricks
• Python
• PL/pgSQL
• Statistics
• Additional Information

Principle responsibilities

• Identify valuable data sources and automate collection, loading and transforming processes.
• Developing and maintaining databases, data systems, and data pipelines.
• Efficient Data Engineering (ETL).
• Data investigations and anomaly detection in data.
• Manage scheduled client and business processes and deliverables.
• Undertake pre-processing of structured and unstructured data.
• Analyse large amounts of information to discover trends and patterns.
• Build predictive models and machine-learning algorithms.
• Provide rapid feedback and insight on ad-hoc requests.
• Present information using data visualization techniques.
• Propose solutions and strategies to business challenges.
• Collaborate with client and product development teams.
• Undertaking data collection, pre-processing, and analysis.
• Building models to address business problems.
• Code migration from legacy languages.
• Creating reports and presentations for business uses.
• Correlating similar data to find actionable results.
• Managing and administrating monthly business processes.
• Client and business communication.