Claude prompts for data engineer job searches

I am applying for the data engineer role below. Rewrite my resume to match it using only experience that is already on my resume. Do not invent tools, data volumes, pipeline counts or systems I have not run. The posting may ask for specific warehouses, orchestration tools, streaming, data modeling, dbt, Spark or data quality work; for each requirement, find the closest real evidence in my resume, and if there is none, put it under "Gaps" rather than writing it in. Output: a table with the columns requirement, evidence, rewritten bullet; a revised one-page resume in plain text where each bullet names the system, the scale if my resume gives it, what I did and the effect on reliability, freshness, cost or the people downstream; and the Gaps list with one honest sentence per gap. Keep every job, title and date unchanged.

Job description:
[paste job description]

My resume:
[paste your resume]

Write a cover note of at most 120 words for the data engineer role below. Use only facts from my resume and my notes. No invented results, no "passionate", no "robust and scalable". Structure: one sentence that shows I understand the data problem this team has (the sources, the scale, or who depends on the data; take it from the job description), two sentences on the most relevant pipeline or platform I have built and what it changed for the people downstream, with a number if I have one, and one closing sentence with a specific ask. Then give a 50-word version for a LinkedIn message. Sentence case, plain words, no exclamation marks.

Job description:
[paste job description]

My resume:
[paste your resume]

Why this team, in my words:
[two or three lines]

Act as the interviewer for a 45-minute data engineer technical screen at the company below. Ask one question at a time and wait for my answer. Cover, in order: two SQL questions on a small schema you define first (one with joins and aggregation over a time window, one with a window function or deduplication), one Python question on processing records (for example parsing and validating messy input), two fundamentals questions drawn from the job description (for example idempotent pipelines, incremental versus full loads, partitioning, slowly changing dimensions, backfills, or exactly-once versus at-least-once delivery), and one question about a pipeline on my resume. After each answer, score it 1 to 5, give a strong answer, and name the one thing I should have said. End with three topics to review tonight.

Job description:
[paste job description]

My resume:
[paste your resume]

Run a data system design interview with me for the role below. Pick a problem that fits the company (for example an event pipeline from a mobile app to a warehouse, a daily pipeline that must finish before the finance team starts, or a change data capture feed from a production database) and describe it in two sentences. Guide me one stage at a time, waiting for my answer: requirements including freshness, volume and who consumes the data, ingestion and the failure modes of the sources, storage layout and partitioning, the data model, orchestration and dependencies, idempotency and backfills, data quality checks and what happens when they fail, cost, access control, and how downstream users find and trust the data. After each stage, point out what an experienced data engineer would have raised that I missed. Finish with a one-page summary in my corrected words. Do not give the full answer up front.

Job description:
[paste job description]

What I know about the product:
[a few lines]

Help me prepare behavioral answers for a data engineer interview using only the situations I describe below. Do not add details; if a story is missing an outcome or a number, ask me for it. First list the eight questions most likely for this role and company (a pipeline failure that affected a business decision, a bad data problem found late, a stakeholder who wanted data faster than was safe, a migration, disagreeing about a data model, a mistake you owned, prioritizing between many requests, and improving something nobody asked you to). Then map each to one of my stories and write an answer of 150 to 200 words in the shape situation, action, result, what I would do differently. Flag where the same story is used twice so I can spread them out.

Job description:
[paste job description]

My stories, rough notes are fine:
[three to five real situations]

Turn my notes below into a portfolio write-up of 500 to 700 words about a data pipeline or platform project, first person, plain language, aimed at a hiring manager. Use only what is in my notes. Where something important is missing (the sources, the volume, the freshness requirement, the tools, the before and after numbers for reliability, latency or cost, what broke), insert a placeholder in square brackets and list the placeholders at the end. Structure: the problem and who it hurt, the constraints, the design and the alternative I rejected and why, how I handled failures and backfills, the measured effect, what went wrong or what I would change, and what came next. Suggest a title, a two-sentence README summary, three honest resume bullets, and one diagram I should draw with its boxes named.

Project notes:
[paste notes, design docs, postmortems or a rough description]

I have an offer for a data engineer role. Below are the offer details, my current compensation, any competing offers and what I have found from public sources. Help me evaluate and negotiate honestly; do not invent market data, and tell me what to look up if you need a number I have not given. Output: (1) a table of four-year total compensation under a cautious and an optimistic equity assumption; (2) which components are most negotiable for this kind of company and why; (3) a short script for the call that asks for a specific number with a reason tied to my experience, in a collaborative tone; (4) questions to ask before accepting, including the level, vesting, on-call expectations, the size of the data team and who it reports to, the state of the current stack, and how success is measured. Under 500 words.

Offer:
[base, bonus, equity and vesting, signing bonus, location]

My situation:
[current compensation, competing offers, constraints]

Write a 30/60/90-day plan for me as a new data engineer on the team below. Keep it realistic: I have to learn the sources, the warehouse, the orchestration, the consumers and the on-call process before changing anything. Days 1 to 30: which pipelines matter most and why, how failures are detected today, who the top consumers are and what they distrust, and the data model as it actually is. Days 31 to 60: a first contribution, for example a flaky pipeline fixed, a data quality check added where a bad load hurt someone, a documented table, or a cost saving. Days 61 to 90: a measurable project with a success metric and risks. For each block, give three to five outcomes rather than activities and one question to ask my manager in week one. Mark assumptions I should verify. Under 450 words.

Job description:
[paste job description]

What I learned about the team in interviews:
[notes]

Give me a data modeling drill for a data engineer interview at the company below. Describe a business in four or five sentences (for example a subscription product with plans, trials, upgrades, refunds and usage events) and list the five questions analysts most need answered. Then ask me, one at a time and waiting for each answer: what the grain of the main fact table is, which dimensions I need and which change over time, how I would handle a slowly changing dimension such as a plan change, how I would model events versus state, which keys are natural and which are surrogate, how I would partition for the expected queries, and how I would test the model for duplicates and missing rows. After each answer, tell me what I missed and give the version a strong candidate would give. Finish with the full schema as table definitions and a checklist I can reuse for any modeling question.

Job description:
[paste job description]

What I know about the product:
[a few lines]