data-manager

name

data-manager

description

Manage data programs, governance operations, and data reliability. Cover data roadmaps, stakeholder coordination, metadata stewardship, lifecycle management, monitoring, incident response, capacity planning, and SLA frameworks. Triggers on "manage data team", "data roadmap", "governance review", "data incident", "SLA framework", "data ops", "stewardship", "data product delivery", or "data KPIs". Human annotation/labeling platform PM: product-management-human-data-platform.

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:

Meeting	Frequency	Attendees	Purpose
Data Leadership Sync	Weekly	Data leads, PMs	Blockers, priorities, resource allocation
Stakeholder Reviews	Bi-weekly	Business sponsors	Roadmap alignment, value demonstration
Sprint Planning	Bi-weekly	Engineering team	Commitments, estimation, dependencies
Retrospectives	Monthly	Full data team	Process improvements, team health

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:

Activity	Frequency	Owner	Output
Metadata stewardship	Continuous	Data stewards	Enriched catalog, documented lineage
Access reviews	Quarterly	Security + owners	Approved access matrix
Data lifecycle enforcement	Monthly	Operations	Archived/deleted per retention policy
Quality SLA review	Monthly	Governance lead	Quality scorecard, remediation plan
Policy compliance audit	Quarterly	Audit/compliance	Gap report, remediation tickets

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:

Layer	Metrics	Alert Threshold
Infrastructure	CPU, memory, disk, network	>80% for 5 min
Database	Connections, lock waits, replication lag	Replication lag >30s
Pipelines	Success rate, duration, row counts	<95% success rate
Data quality	Null rate, freshness, duplicates	SLA breach
Cost	Daily spend vs budget	>110% of daily budget

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:

Category	Metric	Target	Measurement
Reliability	Pipeline success rate	>99%	Airflow/Dagster logs
Quality	Data quality score	>95%	dbt tests + Great Expectations
Freshness	Data latency (source → warehouse)	<4 hours	Pipeline metadata
Cost	Cost per TB processed	Trend down	Cloud billing
Productivity	Time from request to production	<2 weeks	Jira/Asana cycle time
Adoption	Active data consumers	Grow 10% QoQ	BI tool usage logs

SLA tiers:

Tier	Description	RTO	RPO	Example
Tier 1	Business-critical dashboards	1 hour	0	Revenue reporting
Tier 2	Operational analytics	4 hours	4 hours	Marketing attribution
Tier 3	Research/exploratory	24 hours	24 hours	Ad-hoc analysis