The future of Data Engineering

Data Engineering

Not very long ago, business was more than happy to work with day-old data for marketing, reporting, and dashboards. It was termed as a green execution when overnight processes were completed before 8 am next in the morning.

The job of an ETL developer was tedious and exhausting. To wake up in the middle of the night to run end-of-month jobs, running database analysis and index updates at slow hours to save precious computing during daytime as usual.

In 2022, thanks to Snowflake, Databricks, BigQuery, and other amazing big data computing engines that offer cloud-based data warehousing, things are easier and more scalable. Movement from on-prem to cloud has made things faster and more cost-effective.


A change in tide can be observed in the rise of data reliability engineering and data engineering being responsible for managing data infrastructure and overseeing the performance of cloud-based systems. Data Engineering role and data governance role is no longer siloed role as gatekeeper of all the data across the organisation. The roles are now distributed and decentralised. Different teams own the data they produce.


The openness in data sharing needs more control and communication in case of changes. Lack of process around change management can lead to technical and cultural issues. DevOps and DataOps are helping modern-day Data Engineers to tackle data drift and increase data reliability.

Data lineage and observability help Data Engineers in fixing problems quicker and formulate impact analysis. It also gives end-to-end visibility of the flow of data from source to landing to transform and finally to the consumers.

The role of a data engineer is becoming more and more horizontal and focused on data reliability, operations, performance, and cost-efficiency. Data Engineers are moving away from day-to-day ad-hoc querying of data to making sure that data is trustworthy, accessible and secure at each point in its lifecycle.


DataOps makes the data engineers’ life easy, in addition to cloud data warehouses, data lakes allow for even more complex and nuanced processing use cases. Data observability automates many rote and repetitive tasks related to data quality and reliability, providing a baseline of health that enables smooth operations throughout an entire data organisation.

With rise of DataOps tools like StreamSets, Data Engineers have a fantastic opportunity to treat data as a product. Operational, scalable, observable, resilient, and scalable data systems can only be built if the data itself is treated as an evolving, iterative product.

Are you ready to take over this challenge?

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