Firstly, as a Data Engineer myself, I can tell you that there are so many tools to worry about and only a handful you will actually use!

Secondly, as you research and shortlist job opportunities, you’ll come across a lot of tools. Hadoop, Spark, AWS Lambda, TensorFlow, Kafka, Google Pub/Sub, Airflow, Hive, Azure Data Lake, Kubernetes and a few more. Some even include several languages, frameworks, and tools in one opening!

The reality is you’ll never need to know most and definitely not all.

You can identify the tools that align with your career goals with these 3 simple steps.

• Be aware of the elements of a data science platform
• Understand traditional data science roles & responsibilities
• Focus first on companies — not job titles

Data Science Platform elements

It is important to be aware of the elements of the data science platform in a company. A platform can be on-premise, entirely on Cloud or partly on Cloud (Hybrid). Amazon Web Services (AWS), Microsoft Azure (Azure) and Google Cloud Platform (GCP) are some of the most widely used cloud platforms.

Andreas Kretz, a LinkedIn top voice data engineer, created a blueprint to understand any data platform. He eliminates all tools to allow adjustments as per the changing requirements of a company.

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