Project Management for Data Projects

Data work has its own shape

Data projects borrow a lot from software engineering, dependencies, code, something that has to actually run correctly before it's "done", but the vocabulary is different, and vocabulary matters more than it sounds like it should. A generic board with To Do, Doing, Done doesn't capture that a pipeline stalls at ingestion when a source system changes its schema without warning, or that a dashboard isn't finished when the query works, only when a stakeholder trusts the number.

The other recurring problem is that a data project's real dependency graph, this transformation needs that table, this model needs that feature, this dashboard needs both, rarely matches the flat task list a generic tool gives you. Work gets marked done that quietly isn't, because the thing downstream of it hasn't been checked yet.

ShipSprint doesn't force data work into an engineering-shaped box or a generic one. Boards, columns and templates can be set up around a pipeline's actual stages, and for teams that keep pipeline code in git, the board can track that too. That's the concrete difference here: the columns say "ingestion" and "validation" instead of "to do" and "done," so the board actually matches how the work fails.

Where data projects quietly diverge from engineering

The overlap with software engineering is real, both involve dependencies, code review and something that has to run correctly, but the failure modes differ in a way that matters for planning. An engineering bug usually announces itself: something errors, a test fails, a user complains. A data problem is often silent: a transform runs without error and produces a subtly wrong number that nobody notices until a stakeholder asks why a dashboard doesn't match what finance already knew. That changes what "done" needs to mean on a data board, not just "ran without error" but "validated against something," which is worth its own column rather than being folded into delivery.

Ingestion, transformation, delivery, as columns, not afterthoughts

Set up a board around the stages that actually describe how data moves: source ingestion, cleaning and transformation, model or dashboard build, validation, delivery. Per-column WIP limits stop the common failure mode where five sources are "being ingested" simultaneously and none of them actually are, someone's just started five things and finished none.

For teams whose pipeline logic lives in code, dbt models, Python transforms, whatever the stack, the GitHub integration means a branch against a task moves its card, and a merged pull request closes it. The board tracks the pipeline the way it tracks a release: by what shipped, not by what someone remembered to update. (Team plan and above.)

Handoffs between data engineering and whoever's downstream

A data project rarely ends with the pipeline team, a model needs to be handed to whoever deploys it, a dashboard needs to be handed to the analyst or the business team who'll actually use it. That handoff is a common place for work to stall invisibly: the pipeline team considers the job done once data lands correctly, but "done" for the person downstream means something else, and there's often no shared record of what was actually agreed. Putting the handoff itself on the board, as a task with an owner, not just an implicit assumption, closes that gap; it becomes something that has to be explicitly marked complete by the person receiving the work, not just the person delivering it.

What tends to break without this

• A dashboard marked "done" that's actually waiting on a data quality check nobody tracked separately
• A schema change upstream that silently breaks three transforms downstream, discovered a week later
• Pipeline code that ships in a merge nobody logged anywhere else, so the board and the repo tell two different stories
• A metric definition that changed and nobody can find when or why, because it lived in a Slack thread instead of a wiki page
• A stakeholder who trusts a number less each time it changes without explanation, because the change was never documented anywhere they could find it

The stakeholder who only sees the dashboard

Most people consuming a data team's output never see the pipeline, they see a dashboard, or a number in a slide, and they judge the team entirely on whether that number seems right. That's a narrow, high-stakes window into a lot of invisible work, and it means the cost of an untracked delay is different for data teams than for most: a two-week slip in an internal transform doesn't look like a data problem to the stakeholder, it looks like the team being unresponsive, because the only visible signal was the dashboard not updating.

A board with forecasts changes what the stakeholder actually needs to be told. Instead of silence followed by a late dashboard, a data lead can say, honestly, and early, that a source delay has pushed the delivery date, with a specific new estimate behind it. That's a materially better conversation, and it's only possible if the delay was visible on the board before it was visible in the missing dashboard.