Welcome to Analytics Engineering Career Path
About this Career Path
Analytics engineering is an emerging and in-demand career path that combines the fields of data analytics, engineering, and software development. As an analytics engineer, you play a crucial role in transforming raw data into actionable insights that drive data-informed decision-making within organizations.
In this career path, you are responsible for designing, building, and maintaining the infrastructure and systems required for data analysis and reporting.
As analytics engineers, you will work with a variety of tools and technologies, including data warehouses, ETL (Extract, Transform, Load) processes, SQL databases, cloud platforms, and programming languages such as Python. They also collaborate with cross-functional teams to ensure data quality, security, and governance practices are implemented effectively.
In addition to technical skills, analytics engineers possess strong problem-solving, communication, and collaboration abilities. They have a deep understanding of data structures, data modeling, and analytics techniques. They continuously stay updated with the latest advancements in data engineering and analytics tools to deliver innovative and scalable solutions.
For 3-Modules
For 3-Modules
No prior knowledge required
What you will learn
Data Management: Gain expertise in managing and manipulating large datasets, data modelling, and database systems (SQL and NoSQL).
ETL (Extract, Transform, Load) Processes: Learn to design and implement efficient ETL processes to extract data from various sources, transform it into a usable format, and load it into data warehouses or data lakes.
Data Warehousing: Acquire knowledge of data warehousing concepts and technologies, including data schema design, dimensional modelling, and data aggregation techniques
Data Pipelines: Build robust data pipelines to automate data extraction, transformation, and loading tasks, ensuring data integrity and reliability.
Cloud Computing: Familiarize yourself with cloud platforms such as Amazon Web Services (AWS)
Data Visualization: Learn to effectively communicate insights by creating meaningful visualisations and dashboards using tools like Power BI
Collaborative Skills: Develop strong collaboration and communication skills to work effectively with cross-functional teams, including data scientists, analysts, and business stakeholders.