How to become a Full-Stack Data Engineer ?

You must have heard about Full-Stack software developer. There is another stream evolving around data landscape, named Full-Stack Data Engineer. Basically who can deal with end to end data eco system, from data profiling, analysis to build enterprise level data platform.

Lets discuss key skills required to become Full-Stake Data Engineer.

1)SQL - It sits in the core, want to do anything with data like Data analysis, Profiling, Data Quality checks etc.

2)Databases - Proficiency with at least one RDBMS such as Oracle, MySql, Postgres is must to have. Also understanding of modern day cloud data platforms like Redshift, Snowflake is added advantage.

3)ETL - Data Engineers spends most of the time developing ETL (Extract Transform Load) packages. Understanding of ETL and Data warehousing concepts along with hands-on experience of at least one ETL tool like Informatica, Talend, Matillion is must.

4)Python/Spark - Dealing with verity and volume of data like it becomes essential to develop understanding of any scripting such as Python/Spark adds flavor. Using these along with power of cloud opens up opportunity to build highly scalable and available data platforms.

5)Visualization - "Visualization is the language data speaks" .Organizations will only be able to rip benefits of data platform using proper visualization/reporting. It helps finding patterns, getting hidden information and thus taking business decisions.

6)Orchestration - For proper functioning of all these components we need some technology or tools where dependency, schedules, steps can be configured. Such as Airflow, crontab

7)Cloud technologies - For modern day storage, compute, server less, orchestration needs one must be aware of services offered by cloud platform such as AWS, Azure, GCP. It helps choosing right technology for your use case.

I would encourage budding data enthusiast to understand these terms and try to evaluate how you are using these in current setup. Feel free to DM me incase you want to dig deeper or evaluate any use cases.

Happy Learning !!!