Overview: This one-year roadmap aligns to three business objectives I researched: increase revenue by enabling data-driven product improvements, reduce operational costs through automation, and improve time-to-insight for analytics and ML teams. Priorities chosen to deliver high ROI early while building platform maturity.
- Foundational Data Platform (Months 1–4, Effort: 8–10 wks core + ongoing)
- Deliverable: cloud-native data lake + managed data warehouse (e.g., S3 + Snowflake/BigQuery)
- Success metrics: reliable daily ingest (>99% success), query SLA <2s for 80% dashboard queries
- Hires/roles: Senior Cloud Data Engineer (1)
- Reporting: biweekly infra health dashboard to CTO/Product
- Robust Ingestion & Streaming (Months 2–6, Effort: 8–12 wks)
- Deliverable: Kafka/managed streaming + idempotent ingestion pipelines
- Metrics: end-to-end latency < 5 min for event streams; 99.9% message delivery
- Hires: Streaming Engineer (1) or upskill an existing engineer
- Reporting: monthly latency & error trend to analytics/product
- ETL Modernization & Orchestration (Months 3–7, Effort: 6–10 wks)
- Deliverable: Airflow/DBT standardization, modular transforms, lineage
- Metrics: pipeline reusability score, time-to-deploy ETL change < 1 day
- Roles: Data Engineer (2) with DBT experience
- Reporting: sprint demos + deployment cadence to Product Managers
- Data Quality & Observability (Months 4–9, Effort: 6–8 wks)
- Deliverable: Open-source/proprietary data quality checks, monitoring, alerting
- Metrics: data quality issue MTTR < 24 hrs; reduction in analyst data complaints by 70%
- Roles: Data Reliability Engineer or shift-left responsibilities
- Reporting: weekly quality KPIs to analytics leads
- Cataloging & Governance (Months 5–10, Effort: 8–12 wks)
- Deliverable: Data catalog, lineage, access controls, PII classification
- Metrics: percent of critical tables documented >90%; audit readiness
- Roles: Data Steward (0.5 FTE) + Governance council
- Reporting: monthly compliance/status report to Legal/Exec
- Self-Serve Analytics & Feature Store (Months 7–12, Effort: 10–14 wks)
- Deliverable: curated marts, semantic layer, feature store for ML
- Metrics: analyst query time reduction 50%; ML feature reuse rate
- Roles: Analytics Engineer (1), ML Infra Engineer (1)
- Reporting: quarterly business-impact review with product and revenue metrics
- Cost Optimization & SRE for Data (Ongoing, Effort: 4–6 wks initial + ops)
- Deliverable: cost dashboards, lifecycle policies, autoscaling
- Metrics: storage/compute cost per TB reduced 25% YoY
- Roles: SRE/Data Platform (add to existing infra)
- Reporting: monthly finance + exec summary
Prioritization rationale: Platform + ingestion first to unblock all downstream work; quality and governance next to protect decisions; self-serve and feature store later to drive business impact.
Stakeholder reporting cadence:
- Weekly: engineering sprint highlights to Product owners
- Biweekly: infra health + incident summary to CTO/Head of Data
- Monthly: KPI dashboard (ingest reliability, latency, data quality, cost) to Execs with one-line implications
- Quarterly: business-impact review tying data initiatives to revenue, retention, or cost savings; roadmap re-prioritization session with Product/Finance
Success governance: Define OKRs for each initiative, executive sponsor for each, and monthly risk register.
