Month 0–1 (onboard & learn): map data domains, systems, SLAs, major consumers and pain points; run a quick “data health” audit (pipeline success rates, lag, data quality alerts). Set 3-month OKRs: ship 2 high-impact features, reduce P1 incidents by X%, and complete two learning goals.
Month 1–3 (stabilize + small wins): prioritize quick wins from audit; build or improve alerts/runbooks; add lightweight data contracts. Month 4–6 (scale & deliver): deliver larger pipelines/features, automate tests, harden monitoring, and hand off runbooks.
Weekly rituals
- Mon: 30–60m planning — pick top priorities aligned to OKRs (with RICE scoring).
- Tue: Deep-focus blocks (3–4 hours) for coding/features — calendar-blocked.
- Wed: Dev sync / pairing + office hours for stakeholders.
- Thu: Tech maintenance (tests, infra), plus 1-hour learning (courses/articles).
- Fri: 1-hour incident / postmortem review and demo of progress.
- Daily: 1–2x short check-ins for blockers.
Prioritization frameworks
- RICE for new features (Reach, Impact, Confidence, Effort).
- SLO-driven triage for reliability work: use error budgets to decide whether to prioritize reliability vs features.
- Dependency & consumer-first filter: prioritize items blocking data consumers or analytics use-cases.
- Cost-of-delay + risk assessment for technical debt.
Metrics I track
- Feature throughput (lead time), pipeline success rate, data latency, # of P1/P2 incidents, MTTR, consumer satisfaction.
Communicating trade-offs
- Present concise trade-off briefs: impact vs effort chart + SLO risk and proposed mitigations.
- Offer options (fast/partial vs full solution) with timelines and residual risk.
- Use decision logs and owner-assigned action items so stakeholders can approve or reprioritize.
- When escalating, quantify business impact (analytics blocked, revenue risk, SLAs missed) and recommended course with contingencies.
Professional growth
- Allocate two 2-hour blocks per week for learning and code reviews; present one demo or learning summary per month. Solicit quarterly feedback from manager tied to OKRs.
This plan balances delivery, reliability, and growth by using measurable goals, predictable weekly rhythms, SLO-driven prioritization, and clear, data-backed communication of trade-offs.
