How do you balance data engineering tasks and priorities with business and user requirements?

Successful data engineering depends on a deep understanding of business objectives. Before diving into technical implementations, it's crucial to define the project's goals and desired outcomes from the business users perspective. By identifying key performance indicators (KPI’s) and analyzing how data will be utilized, data engineers can prioritize tasks effectively, ensuring maximum value and avoiding unnecessary work. Aligning technical efforts with business needs is fundamental to any project's success.

As Data Engineers gain experience, one of the most important things they learn is that data is not meant to be locked up in warehouses and data lakes. The whole point of efficient Data Engineering solutions is the instant usability and efficient serving of clean and curated data to downstream users who need it. Therefore before undertaking any coding/design/data pipeline work, data engineers need to make sure they are meeting representatives from all stakeholder teams to generate an agreement over the rationale and expected value of the end data product. This is not very different from any software product brainstorming sessions, but have data-specific nuances that need to be paid attention to.

Some of the key stakeholders that are often overlooked are the Data Stewards and Data Quality teams. The best data solution fails if the data being presented can't be trusted. Be sure to involve these teams in your stakeholder sessions.

It is always better to have SLAs with the stakeholders. It helps in determining the design of the pipelines, how frequent the data needs to be updated etc. Having SLAs builds a better understanding and contract among the Data teams and Stakeholders to agree upon.

Smart Data Engineers design end-to-end "data solutions", not "data pipelines". And such solutions require more than just ELT, Data Warehousing and Orchestration knowledge. End to end solutions require constant interaction with software engineers to ensure that the Front-end, Back-end applications, APIs that make use of the data-serving, are as working as expected; with the product managers and data analysts to make sure the "data product" is generating the intended value, or take corrective actions otherwise.

The way I always found helpful is to streamline task based on their priority of delivery - Talk with superiors to discuss priorities - Check weather any dependencies that blocking tasks - Keep task status updated with comments on progress to have better visibility to stake holders - Always communicate risks and blockers with wider audience and get them resolved asap - If technical complexity then do not hesitate to ask

By building quality gates in your automation, and using realistic data wherever possible, you'll find and highlight issues in the solution you're building. The more quickly these are found, the quicker and cheaper to solve.

Automation is pretty much a minimum viable product for most Data Engineering tasks. There is limited value in listing down "standard operating procedures" that need to be executed manually. A data engineer needs to be well-versed with popular workflow-orchestration solutions (including but not limited to tools like Airflow - Astronomer/MWAA, Prefect, Dagster etc) to create either a scheduled DAG or a one-click execution of data workflow. A workflow has of course, individual granular tasks that first need to be cleaned up, standardized, edges ironed out before we take them to the automation step. The key mantra is - simplify, simplify, simplify! Once steps are simplified enough, the automation is much more robust to usage pattern changes.