HeyJaredThe Databricks Data Engineer
Advance to senior Databricks Data Engineer: interview like seniors, execute like seniors, think like seniors
- Indexed issues, last 90 days
- 13
- Latest publication
- Oct 1, 2026
- Audience
- Checking…
- Earliest in this view
- Aug 31, 2026
Latest issues
Which Data Quality Check Fits Each Databricks Pipeline Stage (opens the original)
Read excerpt
Most Databricks teams do not have a data quality problem. They have a placement problem.The same not-null rule sits on the raw table as a warning, again inside the silver transform, and again as a post-load check on gold. When the upstream column finally breaks, three alerts fire at once and not one of them says which hop lost the value. The check count… Read more
The Six Checks a Senior Runs on Your Databricks Pipeline (opens the original)
Read excerpt
A pipeline that has run every night for fourteen months has not been reviewed. It has been left alone.That distinction is where self-assessment goes wrong. Uptime is the cheapest signal a pipeline emits and the one most engineers grade their own work on. The senior engineer who inherits it opens six things in a fixed order and has a verdict inside ten minutes, and it almost never turns on the transformation logic. It turns on what the pipeline does on the days it is not having a good one.So run
Can You Reach Senior Databricks Engineer Without Ever Leading an Architecture Review? (opens the original)
Read excerpt
Sam is four years into a Databricks role, up for senior this cycle. Merged pull requests on the ingestion layer. The nightly job he rescued at three in the morning when the orders table stopped landing. Then his manager asks for one call where the answer wasn't obvious, he framed it, and he lived with the result. Sam starts three different sentences. None of them finish.The packet is full of work and empty of decisions. Sam thinks what he's missing is a chair at the architecture review table. It
How Lakebase Runs Postgres Straight on Object Storage (opens the original)
Read excerpt
Your transactional database and your lakehouse have never shared a single byte.One holds the rows your app writes. The other holds a copy some pipeline made an hour later. Every staleness argument in your standup, every reverse-ETL job you babysit, every “the dashboard says something different from the app” ticket traces back to that gap.Lakebase is Dat… Read more
The Five Rules That Split Exploration From Production Code (opens the original)
Read excerpt
Every Databricks team ships its first production pipeline as a notebook, and that is not the mistake.The mistake shows up six months later. The notebook is 900 lines, three scheduled jobs point at it, and nobody can change the deduplication logic without attaching a cluster and re-running everything above cell 40 to find out whether it worked. The team knows this is bad. The fix everyone proposes, move it all to Python files, is also wrong, because the reason that notebook exists is that someone
Publishing over time
Last 90 days. Choose a month to open its work.
Recurring subjects
Named in the text we hold. One piece can cover several.
Audience
No verified audience measurement yet.
About this data
Counts cover the work we have indexed. Tone needs enough text and a confident classification. Excerpts and episode notes are not full articles or transcripts.
Identity or attribution wrong? Suggest a correction.