Part 1 — Build your foundation
The goal is clarity: a target role, a target company list, and proof that matches what hiring teams actually screen for.
Choose a role track (and commit for 8–12 weeks)
Most candidates lose time by preparing “a bit of everything”. Pick one track, then make your resume, portfolio, and prep match that job’s loop.
Tip: if you’re unsure, start with your strongest proof today (projects, past work, or shipped outcomes) and choose the track that your proof already supports.
Build portfolio proof that looks like real work
Hiring signals are increasingly outcome-based. Your portfolio should show decisions, tradeoffs, and reliability—not just a notebook.
What to include
- A clear problem statement and success metric
- Readable repo structure and a short “decision memo”
- Tests or validation checks (data quality, unit tests, evaluation)
- Monitoring/observability plan (even if it’s lightweight)
What to avoid
- 10-page READMEs with no results
- Copy-paste tutorials with no changes
- “Model accuracy” with no baseline or business context
- Unreproducible notebooks with hidden steps
If you want a quick direction, start from the “Role Playbooks” section on Market Insights and build one project that matches the playbook.
Resume + LinkedIn that convert (simple rules)
- Lead with impact: “what changed” + “how measured” + “scope”. Example: Reduced pipeline cost 22% by partitioning and incremental loads; validated with BigQuery slot usage and SLA metrics.
- Mirror the job description language for the core tools (Python, SQL, dbt, Spark, AWS, etc.).
- Keep a “skills” section short and credible—only list what you can defend in an interview.
- Add one portfolio link that is easy to review in 2 minutes (demo + readme + results).
Target companies using the leaderboard method
- Start with the Leaderboard and filter by industry/region that fits your constraints.
- Pick 20–40 companies to target for 4 weeks.
- For each company: one role, one hiring manager or team lead, one proof artifact to send (project, write-up, or case study).
- Track outcomes (reply rate, screens, onsites) and tighten your target list weekly.
This reduces random applications and increases “signal density”: each application is better aligned and easier to follow up on.
A weekly job search system (repeatable)
Mon–Wed (pipeline)
- 15–25 targeted applications
- 5 follow-ups on last week’s applications
- 2 warm intros or recruiter messages
Thu–Sun (signal)
- Interview practice (SQL/ML/case) 3–5 hours
- Portfolio iteration 2–4 hours
- One “public artifact”: a post, small demo, or write-up
Part 2 — Interviews and offers
Treat interviews like a product. You’ll improve fastest by practicing the exact formats companies use for your track.
Common interview loops (by role)
Data Engineer
- SQL + debugging
- Pipeline/system design (reliability, cost, change management)
- Behavioral (ownership, incidents, stakeholder alignment)
Data Scientist / ML Engineer
- Experiment design + metric reasoning
- Model evaluation and tradeoffs
- ML system design (data → training → inference → monitoring)
For deeper prep, start here: career guides on the blog.
Prep checklists (fast + realistic)
SQL (all tracks)
- Joins, window functions, and grouping edge cases
- Define metrics carefully (cohorts, time windows, exclusions)
- Explain performance basics (indexes, partitioning, filters)
ML/DS (evaluation + product sense)
- Baseline → iterate → validate (avoid leakage)
- Metric choice and tradeoffs (precision/recall, calibration)
- How you monitor quality in production
Data engineering (reliability + ownership)
- Data modeling tradeoffs (star schema vs wide tables)
- Freshness, lineage, and alerting strategy
- Incident story: detection → mitigation → prevention
Behavioral (storytelling)
- Have 6–8 STAR stories ready (impact, conflict, failure, leadership)
- Quantify outcomes and your exact role
- Be clear on tradeoffs you made and why
Remote interview setup (don’t lose points on basics)
- Test audio/video and screen sharing the day before.
- Keep a one-page “reference sheet” (projects, metrics, key examples), but don’t read it.
- For coding/SQL, narrate your assumptions and validate edge cases.
Evaluate the offer (beyond the headline salary)
- Scope: what you own in the first 90 days.
- Data/ML maturity: pipelines, quality checks, monitoring, and governance.
- Manager and team: feedback cadence, clarity, and decision-making.
- Growth: does this role build skills that compound?
Negotiation basics
- Negotiate total comp and scope, not just base salary.
- Ask for the leveling rubric and what “great” looks like in 6 months.
- Use comparisons carefully: similar scope, similar location, similar seniority.
If you want salary context, start with the directional benchmarks on Market Insights and validate with recent postings.
Part 3 — Succeed after you land the role
Your first 90 days are an “interview with receipts”. Make progress visible and tie your work to reliability and business outcomes.
30–60–90 plan (simple template)
First 30
- Learn domain + data definitions
- Ship one low-risk improvement
- Set a weekly check-in cadence
60–90
- Own a medium-scope project end-to-end
- Define success metrics and monitoring
- Document decisions and tradeoffs
Performance + promotion (what managers reward)
- Reliability: fewer surprises, better monitoring, clearer runbooks.
- Communication: crisp updates, explicit tradeoffs, clear asks.
- Leverage: you unblock others and improve systems, not just deliver tasks.
Switching roles (and exiting well)
- Internal transitions are easiest when you already shipped cross-team work.
- Ask for a recommendation when your work is fresh and measurable.
- When resigning: keep it short, document handoff, and preserve relationships.
FAQ
Should I apply broadly or narrowly?
Start narrowly for 2–4 weeks (better alignment, better follow-up). If response is low, broaden the company set—but keep the role track consistent.
Do I need a cover letter?
Usually not. If you write one, keep it short and specific: why this company + why this role + proof you can do the core task.
What’s the fastest way to stand out?
Ship one “reviewable” project: a small demo, results, and a decision memo. Make it easy for a stranger to understand in 2 minutes.
Where should I start if I’m switching careers?
Pick the track with the shortest gap from your past work, then build one portfolio project that mirrors the job loop for that track.
Conclusion
Your best advantage is focus: one track, one target list, one proof artifact, and repeatable practice. If you want a starting point today, read the insights, pick a role playbook, then browse jobs and apply with intent.