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Data Engineering PM 2026 | Pipeline→10 Tasks Visible

ETL invisible: 'Add column' = analysis + migration + backfill + downstream (10 tasks). Show stakeholders pipeline scope, not 'slow.' Free trial.

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Data Engineering PM 2026 | Pipeline→10 Tasks Visible

Data Engineering Is Invisible Work Data engineering reality: ├─ ETL pipelines (extract, transform, load) ├─ Data warehouse design ├─ dbt models and transformations ├─ Airflow/Dagster DAG development ├─ Data quality monitoring ├─ Feature engineering for ML ├─ Analytics table maintenance ├─ Infrastructure scaling ├─ Schema migrations ├─ Data governance compliance When it works, nobody notices.

When it breaks, everyone notices. Why Traditional PM Fails Data Teams 'Build feature X': ├─ Product team sees: Button on page ├─ Data team sees: │ ├─ Raw data extraction │ ├─ Transformation logic │ ├─ Quality validation │ ├─ Dimensional modeling │ ├─ Aggregation tables │ ├─ Incremental loading │ ├─ Backfill historical data │ ├─ Documentation One task becomes ten.

Traditional PM: 'Data team is slow.' GitScrum for Data Engineering Data-aware tracking: ├─ Pipeline tasks with dependencies ├─ dbt model tasks with schema ├─ Data quality gates ├─ SLA tracking visibility ├─ Git-linked to data repos ├─ Infrastructure work visible Show the iceberg below the surface. Pipeline Development Tracking Pipeline lifecycle: ├─ Requirements (what data needed) ├─ Source analysis (raw data exploration) ├─ Schema design (target structure) ├─ Transformation logic (dbt/Spark) ├─ Quality tests (data validation) ├─ Orchestration (Airflow DAG) ├─ Monitoring (alerts, SLAs) ├─ Documentation (lineage, usage) GitScrum approach: ├─ Pipeline epic with phases ├─ Task per phase ├─ Checklists for requirements ├─ Git commits link to tasks ├─ 'Pipeline: sourceusers → 7/9 phases' dbt Project Management dbt workflow: ├─ models/ directory ├─ Staging models ├─ Intermediate models ├─ Mart models (business logic) ├─ Tests (not null, unique, etc.) ├─ Documentation (schema.yml) ├─ CI/CD (dbt Cloud or custom) GitScrum dbt tracking: ├─ Model tasks linked to Git ├─ 'Add dimcustomers model' ├─ Commit links to model file ├─ Test tasks as subtasks ├─ Schema changes visible ├─ Wiki has dbt standards Airflow DAG Development Orchestration tasks: ├─ DAG design (task dependencies) ├─ Operator implementation ├─ Sensor configuration ├─ Connection setup ├─ Variable management ├─ Testing (local + staging) ├─ Production deployment ├─ Monitoring configuration GitScrum tracking: ├─ DAG task with checklist ├─ [x] Operators defined ├─ [x] Sensors configured ├─ [x] Connections set up ├─ [ ] Staging test pass ├─ [ ] Production deployed ├─ Git commits link to DAG files Data Quality Gates Quality workflow: ├─ Schema validation ├─ Row count checks ├─ Null value thresholds ├─ Uniqueness constraints ├─ Referential integrity ├─ Business rule validation ├─ Freshness checks ├─ Anomaly detection GitScrum approach: ├─ Quality task per pipeline ├─ Checklist = quality checks ├─ [x] Not null: userid ├─ [x] Unique: transactionid ├─ [ ] Freshness < 1 hour ├─ Pipeline blocked until green SLA Tracking Visibility SLA reality: ├─ 'Daily report by 7 AM' ├─ 'Real-time dashboard < 5 min lag' ├─ 'ML features updated hourly' ├─ Stakeholders don't see the work ├─ They see: late or not late GitScrum SLA tasks: ├─ SLA task per commitment ├─ Description: requirements ├─ Linked to pipeline tasks ├─ SLA breach = P0 task ├─ Historical tracking for patterns Make reliability work visible.

Data Warehouse Migration Migration project: ├─ Schema analysis ├─ Table-by-table migration plan ├─ Transformation updates ├─ Parallel running period ├─ Validation per table ├─ Cutover coordination ├─ Rollback plan GitScrum migration tracking: ├─ Migration epic ├─ Task per table/schema ├─ Checklist: migrated, validated, cutover ├─ Dependency graph visible ├─ Rollback docs in wiki Feature Store Development ML feature engineering: ├─ Feature definition ├─ Transformation logic ├─ Online vs offline stores ├─ Backfill historical features ├─ Documentation for ML team ├─ Monitoring feature drift GitScrum approach: ├─ Feature task per feature set ├─ Link to ML team's model tasks ├─ 'userpurchasehistory feature' ├─ Backfill as separate task ├─ Wiki documents feature catalog Schema Change Management Schema evolution: ├─ New column request ├─ Impact analysis ├─ Migration script ├─ Backfill (if needed) ├─ Downstream updates ├─ Documentation update ├─ Communication to consumers GitScrum tracking: ├─ Schema change task ├─ Linked to requestor's feature task ├─ Checklist: analysis, migration, backfill ├─ Consumer notification task ├─ Git commit to migration scripts Cross-Team Dependencies Data team serves: ├─ Product team (dashboards) ├─ ML team (features) ├─ Finance team (reports) ├─ Marketing team (analytics) ├─ Operations team (monitoring) GitScrum coordination: ├─ Request tasks from other teams ├─ Priority visible across teams ├─ Dependencies linked ├─ 'Blocked: waiting for ML feature spec' ├─ No hidden queues Infrastructure Work Tracking Infra tasks: ├─ Cluster scaling ├─ Cost optimization ├─ Performance tuning ├─ Security updates ├─ Upgrade planning ├─ Disaster recovery testing GitScrum approach: ├─ Infrastructure epic ├─ Tasks with priority ├─ 'Upgrade Spark 3.4 → 3.5' ├─ Linked to performance issues ├─ Wiki documents architecture Pricing for Data Teams Solo data engineer: $0 (free) 2-person team: $0 (free) 5-person team: $26.70/month 10-person team: $71.20/month 20-person data org: $160.20/month $8.90/user/month. 2 users free forever.

No data engineering tier. No per-pipeline pricing.

All features included. Toolchain Integration Data stack: ├─ dbt (transformation) ├─ Airflow/Dagster (orchestration) ├─ Spark (processing) ├─ Snowflake/BigQuery (warehouse) ├─ Fivetran/Airbyte (ingestion) ├─ Great Expectations (quality) ├─ DataHub/Atlan (catalog) GitScrum fits: ├─ Git repos (dbt, Airflow DAGs) ├─ Task tracking (project management) ├─ Wiki (documentation) ├─ Doesn't replace data tools ├─ Complements them Documentation in Wiki Data documentation: ├─ Pipeline architecture ├─ Data dictionary ├─ dbt model documentation ├─ SLA commitments ├─ Runbooks (incident response) ├─ Onboarding guides GitScrum wiki: ├─ All docs in one place ├─ Linked from tasks ├─ Searchable ├─ Not in random Confluence pages ├─ Not lost in Slack Real Data Team Experience 'Data engineering is invisible until it breaks.

Our stakeholders thought we "just ran some queries". Now they see: 47 pipeline tasks, 12 quality gates, 8 SLA commitments.

They understand why "add one column" takes a week. The Git integration means our dbt commits show up in tasks automatically.

Finally, visibility for invisible work.' - Data Engineering Lead, Series B Startup Daily Workflow Morning: ├─ Check pipeline status (Airflow/Dagster) ├─ Check board: Any P0 issues? ├─ Update tasks: Progress on current work ├─ Review Git: PRs to review Development: ├─ Pick task from board ├─ dbt/Spark development ├─ Git commits link automatically ├─ Update task checklist ├─ Code review via Git End of day: ├─ Task status updated ├─ Tomorrow's priorities clear ├─ Blockers visible ├─ 10 minutes, done Incident Response Pipeline failure: ├─ Alert received ├─ P0 task created ├─ Investigation documented ├─ Fix task linked ├─ Root cause analysis ├─ Prevention task created ├─ Post-mortem in wiki GitScrum tracking: ├─ Incident visible to stakeholders ├─ Progress tracked ├─ Fix commits linked ├─ Post-mortem accessible ├─ Pattern recognition over time Start Free Today 1.

Create pipeline tasks 4. Make data work visible Data engineering, finally visible.

The GitScrum Advantage

One unified platform to eliminate context switching and recover productive hours.

problem.identify()

The Problem

Invisible infrastructure work - Pipeline work doesn't show up in product backlogs. Stakeholders see 'data is slow'. Don't see the 47 dependencies.

Pipeline dependencies untracked - Task A depends on B depends on C. Traditional PM: flat list. No DAG understanding.

Data quality gates missing - No built-in concept of data validation. Quality work = 'testing'. Not the same thing.

SLA tracking separate - SLA commitments in spreadsheet. Pipeline status in Airflow. Work tracking in Jira. Three places.

Cross-team requests chaotic - Product wants dashboard. ML wants features. Finance wants report. All urgent. No unified queue.

Schema changes invisible - 'Add one column' = analysis + migration + backfill + downstream updates. Stakeholders see: 'one task, took 2 weeks'.

solution.implement()

The Solution

Pipeline work visible - Break down pipeline work into tasks. Stakeholders see: 47 tasks, 32 done, 15 in progress. Context for 'why data takes time'.

Dependencies tracked - Link tasks. Task B blocked by Task A. DAG-like view of work dependencies. Bottlenecks visible.

Data quality gates built-in - Quality checklist per pipeline. [x] Schema valid, [x] Row count OK, [ ] Freshness check. Pipeline blocked until green.

SLA tracking integrated - SLA tasks visible on board. Link to pipeline work. Breach = P0 task. Historical tracking for patterns.

Cross-team queue unified - All requests in one board. Priority visible. Data team capacity visible. No hidden queues in Slack.

Schema changes decomposed - Schema change = epic with subtasks. Analysis, migration, backfill, downstream. Stakeholders see the real scope.

workflow.steps()

How It Works

Connect Data Repos

Link GitHub/GitLab repos for dbt, Airflow DAGs, Spark jobs. Commits link to tasks automatically.

Create Pipeline Tasks

Break pipelines into phases. Quality checklist per pipeline. Dependencies linked between tasks.

Track Quality Gates

Data quality as checklist items. Schema validation, row counts, freshness. Pipeline tasks blocked until quality passes.

Show Stakeholders the Work

47 tasks visible. Progress clear. Dependencies obvious. 'Why data takes time' finally answered.

expertise.verify()

Why GitScrum

GitScrum addresses Data Engineering Team Project Management - Track Pipelines Not Just Tasks through Kanban boards with WIP limits, sprint planning, and workflow visualization

Methodology

Problem resolution based on Kanban Method (David Anderson) for flow optimization and Scrum Guide (Schwaber and Sutherland) for iterative improvement

Capabilities

Kanban boards with WIP limits to prevent overload
Sprint planning with burndown charts for predictable delivery
Workload views for capacity management
Wiki for process documentation
Discussions for async collaboration
Reports for bottleneck identification

Industry Practices

Kanban MethodScrum FrameworkFlow OptimizationContinuous Improvement

features.related()

Related Features

View all features

Git Integrations

Data repo tracking - dbt model commits, Airflow DAG changes, Spark job updates all link to tasks. Code review visible in project context.

Sprint Planning

Ship faster without the chaos. Drag-and-drop backlog prioritization, velocity tracking across iterations, and burndown charts that update as work gets done—not when someone remembers to update a spreadsheet. Your team always knows what's next, stakeholders see progress without asking, and {vertical} across {city} consistently hit their sprint commitments.

Kanban Boards

Pipeline status visualization - See blocked pipelines, quality gate status, cross-team requests. Data work finally visible.

Wiki & Documentation

Data documentation hub - Pipeline architecture, data dictionary, dbt standards, runbooks. All searchable, all linked from tasks.

Team Management

Junior devs shouldn't access client billing. Contractors shouldn't see other projects. Set granular permissions that match how {vertical} actually work—by role, project, or even specific boards. Invite freelancers in {city} with time-limited access, track who did what, and revoke credentials in one click.

Frequently Asked Questions

Still have questions? Contact us at customer.service@gitscrum.com

Does GitScrum integrate with dbt Cloud or Airflow?

GitScrum integrates via Git. Your dbt models and Airflow DAGs are in Git repos. Connect those repos, commits link to tasks. GitScrum doesn't replace dbt Cloud or Airflow - it tracks the project management layer on top.

How do you track data quality gates?

Create a checklist per pipeline task. Items: schema validation, row count check, null threshold, freshness. Check items as tests pass. Pipeline task stays 'blocked' until quality checklist complete. Quality tests themselves run in your data tools (Great Expectations, dbt tests, etc.).

Can stakeholders see data team capacity?

Yes. All work on one board. Filter by data team. See: 47 tasks, 32 done, 15 in progress, 12 blocked. Request queue visible. 'We need this dashboard' goes into the queue, not a Slack DM that gets lost.

How do you handle schema change requests?

Schema change as epic or parent task. Subtasks: impact analysis, migration script, backfill (if needed), downstream updates, documentation, consumer notification. Requestor sees the real scope, not 'add one column = 1 task'.