Senior Data Analyst

4 weeks ago

Johannesburg, Gauteng, Saudi Arabia Experian Full time

Job Description

An experienced Senior Data Analyst who will be responsible for processing, analysing, and interpreting large amounts of data to identify best practices, insights and patterns that will add value and improve our data platform and data offering.

  • To support and manage client delivery and engagement and help us maintain our position as a market leader.
  • The ideal candidate will have strong analytical and statistical expertise in T-SQL, Python and Azure, and possess critical thinking and problem-solving skills to support client delivery and day to day undertakings.
  • They will also have a passion for machine learning and research, keeping abreast of the latest techniques and industry updates and be able to communicate and demonstrate their findings effectively to management and their team.
  • The candidate will engage with other teams on data products and solutions and use their analytical and statistical expertise to transform complex data sets into actionable insights that drive business growth and success.
  • They will perform thorough data validation and testing, to ensure that the data is properly cleansed, transformed, and stored.
  • Implementing quality control measures to ensure data accuracy and reliability throughout the data lifecycle. Attention to detail and commitment to data quality will be critical to the success of the role.

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.

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