Agile and Scrum Fundamentals Questions

Requirements: Safe, scalable dataset changes across teams with minimal bottlenecks.

Governance Model (Roles)

• Dataset Owner: accountable for dataset design and changes (product-aligned).
• Data Steward: ensures schema quality, lineage, privacy/classification checks.
• Approver / Data Council: cross-functional reviewers for breaking changes.
• CI Automation: automated validators and deployment guards.

Release Process & Approval Gates

1. Proposal PR (schema + migration plan) — automatic schema compatibility checks and linters run.
2. Steward review — privacy, PII, and lineage verification.
3. Consumer impact analysis automated via lineage to list dependent jobs; consumers must acknowledge or auto-enroll in migration window.
4. Approver sign-off for breaking changes; non-breaking merges after steward approval.

Automated Validation Checks

• Schema compatibility (Avro/Protobuf rules).
• Data quality tests: null rates, distribution drift, column-level statistics.
• Regression tests: sample-run E2E on staging using consumer contracts.

Versioning Policy

• Semantic dataset versions: MAJOR (breaking), MINOR (additive), PATCH (fixes). Tag datasets in registry; maintain immutable historical versions.

Agile Integration

• Treat dataset changes as backlog items with clear definition-of-ready. Include change tickets in sprint planning; non-urgent breaking changes scheduled in migration sprints.
• Use lightweight Data Council weekly triage to avoid blocking. Provide templates and SLA for reviews (e.g., 48 hours).

Avoid Bottlenecks

• Automate checks to reduce manual approvals. Delegate non-breaking approvals to stewards. Keep a rolling migration window and consumer migration waves.

This model balances safety with velocity by automating validations, clarifying ownership, and integrating governance into Agile workflows.

CI/CD workflow for data pipelines

Pipeline stages:

• Dev: feature branches, unit tests for transformations (pytest/dbt tests), schema linting.
• CI: run unit tests, static analysis, dbt model compilation, Great Expectations tests against synthetic or sample data.
• Integration (staging): deploy to staging, run full pipeline with seeded data, end-to-end data quality checks, schema validation.
• Prod gated deploy: promotion after green integration tests and approvals.

Promotion & rollback:

• Promote artifacts (git tags, container images) across environments. Use blue/green or shadow deploys for producers/consumers.
• Rollback via previous tag, table versioning, or replaying upstream sources; maintain runbooks.

Tools:

• GitHub Actions/Jenkins for CI, dbt for transformation + tests, Airflow/Kubernetes for orchestration, Great Expectations for data quality, Terraform for infra, ArgoCD for deployments.

Gating: require passing unit/GE checks and schema compatibility before automatic promotion; manual approval for prod. Monitor with alerts and automatic remediation playbooks.

When strict Scrum harms delivery

• Heavy ops: unpredictable incidents need immediate attention.
• Long migrations: work spans many sprints and doesn't slice cleanly.
• Deep research: high-uncertainty exploration without predictable outputs.

Principled deviations

1. Scrumban for ops: maintain sprint cadence but use a shared kanban ops board with priority SLAs; reserve capacity and use WIP limits.
   • Guardrails: capacity floor for sprint commitments; weekly ops review; measure sprint predictability.
2. Dedicated ops board + rotating on-call: route incidents via on-call rotation; capture operational work as tickets with SLAs.
   • Guardrails: backlog hygiene, limit on-call duration, blameless postmortems.
3. Timeboxed research spikes / R&D sprints: allocate fixed sprint(s) for exploration with clear success criteria.
   • Guardrails: define exit criteria, deliverables (prototype, decision), and evaluation metrics.

Communication & measurement

• Announce deviation with rationale, expected duration, and success metrics to stakeholders.
• Measure impact via cycle time, throughput, incident count, and delivery predictability to ensure no process drift.

RECOMMENDATION:
I recommend 2-week sprints for a mid-sized data engineering team of six. Two-week cadence balances delivery velocity with time for testing, integration, and stakeholder review across multiple product teams.
TRADE-OFFS:

• 1-week: Pros — fast feedback, tight focus. Cons — high overhead, insufficient time for ETL runs, integration testing, or investigation tasks.
• 2-week: Pros — predictable rhythm, enough time for building ETL jobs, CI/CD, and reviews. Cons — slightly slower feedback than weekly.
• 4-week: Pros — more uninterrupted engineering time for large migrations. Cons — delayed feedback, harder to reprioritize.
  LONG-RUNNING TASKS:
• Break into vertical slices (interface + testable subset). Use spikes for research and create visible umbrella tickets (epic + child tasks) with clear acceptance criteria and milestones.
  SPRINT CADENCE & RELEASES:
• Two-week sprints enable fortnightly releases or staging promotions; feature flags or versioned pipelines allow safe incremental rollouts. Cadence shortens stakeholder feedback loops while preserving time for QA and data validation.

Kanban vs Scrum for frequent interrupts

Pros/cons:

• Scrum: good for predictability and planned sprints but struggles with frequent unplanned work.
• Kanban: better for flow and handling variable work, continuous delivery; harder to guarantee longer commitments.
• Scrumban: hybrid — sprint cadence with WIP limits for flexibility.

Recommendation: Scrumban

• Rules: set WIP limits per swimlane (e.g., In Progress max 5, Review max 3).
• Swimlanes: Planned Work, Interrupts/On-call, Expedite (P0).
• Urgent handling: define an interrupt policy—if an interrupt estimated > 8 hours, create a ticket, assign on-call and decrease sprint capacity accordingly; if <2 hours allow ad-hoc handling without re-planning.

Operationalize:

• Reserve 20% capacity per sprint for interrupts or enforce strict WIP for interrupts lane.
• Use SLA targets for incident response and count interrupts in velocity reporting to improve future capacity planning.