Data Manager

Overview

Manage data programs, governance operations, and data reliability. This skill covers data roadmaps, stakeholder coordination, metadata stewardship, lifecycle management, monitoring, incident response, capacity planning, and SLA frameworks.

Features

• Data roadmap planning with stakeholder alignment and delivery cadence
• Governance operations: stewardship, access reviews, lifecycle enforcement
• Data ops monitoring with incident response and escalation paths
• Team KPI/SLA scorecards and operational metrics
• Cross-functional coordination across engineers, analysts, scientists, and legal

Usage

1. Identify the user's data management need (roadmap, governance, ops, or coordination)
2. Follow the corresponding workflow below
3. Produce structured outputs: roadmaps, governance policies, incident reports, or KPI dashboards

Examples

• User: "Create a data team roadmap"

Agent: Runs Program Management workflow, produces quarterly roadmap with initiatives, dependencies, and stakeholder sign-offs

• User: "Set up data governance"

Agent: Runs Governance Operations workflow, defines stewardship roles, access review cadence, and lifecycle policies

• User: "Handle a data incident"

Agent: Runs Data Ops workflow, triages severity, executes runbook, produces post-incident report with action items

When to Use

• Own the data roadmap, stakeholder reviews, and data product delivery cadence
• Run governance operations (stewardship, access reviews, lifecycle enforcement)
• Establish data ops monitoring, incident response, and team KPI/SLA scorecards
• Coordinate engineers, analysts, scientists, and legal on cross-functional data work

When NOT to Use

• Deep platform architecture ADRs or ontology design → use data-architect or ontology-engineer
• Hands-on warehouse SQL optimization or SCD modeling → use data-warehouse-engineer
• ML experimentation, model evaluation, or MLOps deployment → use data-scientist
• Cloud VPC, Kubernetes, or IaC provisioning → use infrastructure-engineer
• Company-wide multi-team technical programs (non-data) → use technical-program-manager

Core Workflows

1. Data Program & Product Management

Responsibilities:

• Own the data roadmap aligned to business outcomes
• Translate stakeholder needs into data product requirements
• Coordinate cross-functional data work (engineers, analysts, scientists, legal)

Operational cadence:

Data product delivery checklist:

1. Define the business question and success criteria
2. Identify data sources and validate availability/quality
3. Design the data model (see data-architect skill)
4. Build with observability (logging, lineage, tests)
5. Validate with stakeholders before GA
6. Document and train consumers
7. Monitor usage and iterate

2. Governance Operations Execution

Core activities:

Escalation paths:

• Data incident → On-call engineer → Team lead → Director
• Quality breach → Data steward → Governance committee → CDO
• Access violation → Security team → Legal (if PII exposure)

3. Data Operations & Reliability

Monitoring stack:

Incident response phases:

1. Detect: Alert fires or user reports issue
2. Triage: Assess severity (P1-P4), assign owner
3. Mitigate: Stop bleeding (rollback, redirect traffic)
4. Resolve: Root cause fix deployed
5. Review: Post-mortem within 48 hours for P1-P2

4. Metrics & SLA Framework

Data team KPIs:

SLA tiers: