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#82 - What Every Aspiring Data Engineer Needs to Know in 2025 | Ananth Packkildurai cover
#82 - What Every Aspiring Data Engineer Needs to Know in 2025 | Ananth Packkildurai cover
Let's Talk AI

#82 - What Every Aspiring Data Engineer Needs to Know in 2025 | Ananth Packkildurai

#82 - What Every Aspiring Data Engineer Needs to Know in 2025 | Ananth Packkildurai

58min |20/08/2025
Play
undefined cover
undefined cover
#82 - What Every Aspiring Data Engineer Needs to Know in 2025 | Ananth Packkildurai cover
#82 - What Every Aspiring Data Engineer Needs to Know in 2025 | Ananth Packkildurai cover
Let's Talk AI

#82 - What Every Aspiring Data Engineer Needs to Know in 2025 | Ananth Packkildurai

#82 - What Every Aspiring Data Engineer Needs to Know in 2025 | Ananth Packkildurai

58min |20/08/2025
Play

Description

What do you really need to thrive as a data engineer today?


Ananth Packkildurai joins us to cut through the noise. From his experience building systems at Slack to his current role in data engineering, Ananth reveals what skills truly stand the test of time, like SQL, data modeling, and a deep understanding of user needs. 


We also explore how the rise of Generative AI is changing the game, what observability means in practice, and why chasing the latest trend might not be your best move. 


Clear, practical, and refreshingly honest. 


This episode is for anyone who wants to grow with intention.


Top Insights:

  • Data engineering is at a pivotal moment, similar to the industrial revolution.

  • The demand for data engineering skills is rapidly increasing.

  • Understanding SQL is crucial as it constitutes the majority of data workloads.

  • Feature prioritization should focus on high yield, low effort projects.

  • Observability is essential for building reliable data systems.

  • Scaling challenges often arise from unexpected user demand spikes.

  • Product thinking is important in data engineering to meet user needs.

  • GenAI has the potential to revolutionize data engineering practices.

  • Exploring various aspects of data engineering is beneficial before specializing.

  • Real-world observation can enhance understanding of data engineering concepts.


Connect with Ananth Packkildurai

Connect with Thomas Bustos


Hosted by Ausha. See ausha.co/privacy-policy for more information.

Description

What do you really need to thrive as a data engineer today?


Ananth Packkildurai joins us to cut through the noise. From his experience building systems at Slack to his current role in data engineering, Ananth reveals what skills truly stand the test of time, like SQL, data modeling, and a deep understanding of user needs. 


We also explore how the rise of Generative AI is changing the game, what observability means in practice, and why chasing the latest trend might not be your best move. 


Clear, practical, and refreshingly honest. 


This episode is for anyone who wants to grow with intention.


Top Insights:

  • Data engineering is at a pivotal moment, similar to the industrial revolution.

  • The demand for data engineering skills is rapidly increasing.

  • Understanding SQL is crucial as it constitutes the majority of data workloads.

  • Feature prioritization should focus on high yield, low effort projects.

  • Observability is essential for building reliable data systems.

  • Scaling challenges often arise from unexpected user demand spikes.

  • Product thinking is important in data engineering to meet user needs.

  • GenAI has the potential to revolutionize data engineering practices.

  • Exploring various aspects of data engineering is beneficial before specializing.

  • Real-world observation can enhance understanding of data engineering concepts.


Connect with Ananth Packkildurai

Connect with Thomas Bustos


Hosted by Ausha. See ausha.co/privacy-policy for more information.

Share

Embed

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Description

What do you really need to thrive as a data engineer today?


Ananth Packkildurai joins us to cut through the noise. From his experience building systems at Slack to his current role in data engineering, Ananth reveals what skills truly stand the test of time, like SQL, data modeling, and a deep understanding of user needs. 


We also explore how the rise of Generative AI is changing the game, what observability means in practice, and why chasing the latest trend might not be your best move. 


Clear, practical, and refreshingly honest. 


This episode is for anyone who wants to grow with intention.


Top Insights:

  • Data engineering is at a pivotal moment, similar to the industrial revolution.

  • The demand for data engineering skills is rapidly increasing.

  • Understanding SQL is crucial as it constitutes the majority of data workloads.

  • Feature prioritization should focus on high yield, low effort projects.

  • Observability is essential for building reliable data systems.

  • Scaling challenges often arise from unexpected user demand spikes.

  • Product thinking is important in data engineering to meet user needs.

  • GenAI has the potential to revolutionize data engineering practices.

  • Exploring various aspects of data engineering is beneficial before specializing.

  • Real-world observation can enhance understanding of data engineering concepts.


Connect with Ananth Packkildurai

Connect with Thomas Bustos


Hosted by Ausha. See ausha.co/privacy-policy for more information.

Description

What do you really need to thrive as a data engineer today?


Ananth Packkildurai joins us to cut through the noise. From his experience building systems at Slack to his current role in data engineering, Ananth reveals what skills truly stand the test of time, like SQL, data modeling, and a deep understanding of user needs. 


We also explore how the rise of Generative AI is changing the game, what observability means in practice, and why chasing the latest trend might not be your best move. 


Clear, practical, and refreshingly honest. 


This episode is for anyone who wants to grow with intention.


Top Insights:

  • Data engineering is at a pivotal moment, similar to the industrial revolution.

  • The demand for data engineering skills is rapidly increasing.

  • Understanding SQL is crucial as it constitutes the majority of data workloads.

  • Feature prioritization should focus on high yield, low effort projects.

  • Observability is essential for building reliable data systems.

  • Scaling challenges often arise from unexpected user demand spikes.

  • Product thinking is important in data engineering to meet user needs.

  • GenAI has the potential to revolutionize data engineering practices.

  • Exploring various aspects of data engineering is beneficial before specializing.

  • Real-world observation can enhance understanding of data engineering concepts.


Connect with Ananth Packkildurai

Connect with Thomas Bustos


Hosted by Ausha. See ausha.co/privacy-policy for more information.

Share

Embed

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