Write an engineering job description
intermediateClaude SonnetHR & PeopleRecruitingrecruitingjob-descriptionengineeringhiringhr
Use case
Use this prompt to write a job description for an engineering role that accurately represents the work, the team, and the company — without defaulting to generic requirements that filter out strong candidates. Most engineering JDs are either too vague or too prescriptive; this prompt produces a description that attracts the right people.
The prompt
You are a recruiting partner or engineering manager writing a job description for an engineering role. Context: - Company:{{company_name}}- Role title:{{role_title}}(e.g., Senior Software Engineer, Staff Engineer, ML Engineer) - Team and product context:{{team_context}}(what team this person joins, what they'll be working on) - Seniority level:{{seniority}}(IC1–IC6, or equivalent — junior/mid/senior/staff/principal) - Primary technical responsibilities:{{responsibilities}}(the 4–6 most important things this person will own or contribute to) - Required technical skills:{{required_skills}}(languages, frameworks, systems — be honest about what's truly required vs. nice to have) - Preferred skills:{{preferred_skills}}(genuinely nice to have — not hidden requirements) - Team size and structure:{{team_structure}}- Company stage and culture notes:{{company_culture}}(what makes working here distinct — be honest) - Compensation range:{{comp_range}}(include if known and if policy allows) - Remote/hybrid/office:{{work_location}}Write a job description with these sections: ## Role Overview (3–4 sentences) What this person will do and why this role exists. Specific to the actual work, not generic engineering platitudes. The first sentence should tell a reader what they'll be building, not just that they'll "join a dynamic team." ## What You'll Work On 5–7 specific bullet points describing the primary responsibilities. Written in present tense ("You design and build…"), active and specific. No "other duties as assigned." ## What We're Looking For Split into two sections: - Required: The genuine minimum bar for success in this role. Be honest — if someone without a CS degree can do this job, don't list "BS in Computer Science or equivalent" as required. - Nice to have: Real preferences, not hidden requirements. ## What You'll Find Here (Company/team differentiation) 3–5 bullets on what makes working here distinct from other engineering jobs. Must be specific and honest — don't list "we move fast and break things" if that's not true. ## Compensation and Benefits (if applicable) Salary range, equity, and key benefits. Include this — companies that share ranges see higher application quality and save time for everyone. Tone: direct and specific. The best engineering candidates are skeptical of generic job descriptions — every sentence should tell them something real about the role, team, or company.
Variables
{{{{company_name}}}}Replace with your {{company name}}{{{{role_title}}}}Replace with your {{role title}}{{{{team_context}}}}Replace with your {{team context}}{{{{seniority}}}}Replace with your {{seniority}}{{{{responsibilities}}}}Replace with your {{responsibilities}}{{{{required_skills}}}}Replace with your {{required skills}}{{{{preferred_skills}}}}Replace with your {{preferred skills}}{{{{team_structure}}}}Replace with your {{team structure}}{{{{company_culture}}}}Replace with your {{company culture}}{{{{comp_range}}}}Replace with your {{comp range}}{{{{work_location}}}}Replace with your {{work location}}
Example
Input
company_name: Anchor Analytics role_title: Senior Software Engineer, Data Platform team_context: Joining the 4-person data platform team responsible for the data ingestion, transformation, and serving infrastructure that powers Signal and all customer-facing analytics seniority: Senior (IC3 equivalent — 5+ years experience) responsibilities: Design and maintain Spark-based data pipeline infrastructure; build and scale the data serving layer that powers Signal's real-time insights; own reliability and performance of the core data platform; collaborate with product engineering to define data contracts; lead technical design for new data ingestion connectors; contribute to on-call rotation (currently low volume — ~2 incidents/month) required_skills: Python or Scala (3+ years); experience building and operating distributed data systems (Spark, Flink, or similar); experience with data modeling and schema design; comfort with cloud infrastructure (AWS preferred); ability to lead technical design reviews preferred_skills: Experience with Airflow or similar orchestration; familiarity with dbt; Kafka or streaming systems experience; experience at a SaaS company team_structure: 4-person data platform team; reports to CTO (Priya Patel); close collaboration with 6-person product engineering team company_culture: Pre-Series A startup, 28 people; real ownership — you'll be setting architecture direction, not implementing tickets; strong technical culture led by a CTO with ML background from Palantir; Signal is new and growing fast — the infrastructure decisions you make now will set the foundation for the next 3 years comp_range: $165K–$195K base + meaningful equity (0.15–0.25% depending on experience) work_location: Remote (US) with quarterly in-person team week
Output
# Senior Software Engineer, Data Platform **Anchor Analytics | Remote (US) | $165K–$195K + equity** ## Role Overview You'll design and scale the data infrastructure that powers Signal — Anchor's AI-driven analytics layer that surfaces operational insights for SaaS companies. The data platform team owns everything between raw customer data and the product surface: ingestion, transformation, modeling, and serving. This is a foundational role at a pre-Series A company — you'll be setting architecture direction, not working through a backlog of tickets someone else designed. ## What You'll Work On - Design and operate the Spark-based data pipeline infrastructure that processes event and transactional data from 40+ customer systems - Build and scale the data serving layer that powers Signal's real-time and near-real-time insight delivery - Define data contracts between the platform team and product engineering — including schema evolution strategy as Signal's data requirements grow - Lead the technical design for new data source connectors (CRM, billing, product analytics — we add 2–3 new connectors per quarter) - Drive reliability and performance of the core platform: SLAs, monitoring, alerting, and incident response - Contribute to the on-call rotation (currently ~2 incidents/month; you'll help keep it that way) - Participate in architectural decision-making for the data platform's next 12 months — Signal is growing fast and the infrastructure decisions made now have a long runway ## What We're Looking For **Required:** - 5+ years of software engineering experience, with meaningful time spent on distributed data systems - Strong proficiency in Python or Scala — you write production code, not just glue scripts - Hands-on experience building and operating systems like Spark, Flink, or equivalent at scale - Experience with data modeling and schema design in a production environment - Comfortable working with cloud infrastructure (AWS preferred; GCP/Azure experience also valued) - Ability to lead technical design reviews and communicate architecture decisions clearly to non-platform engineers **Nice to have:** - Experience with Airflow or another orchestration framework - Familiarity with dbt for transformation layer - Streaming systems experience (Kafka, Kinesis) - Prior experience at a B2B SaaS company — you'll find the customer data landscape familiar We don't require a CS degree. If you've built serious data infrastructure and can demonstrate it, we want to talk. ## What You'll Find Here - **Real ownership:** The data platform team makes architecture decisions, not just implementation decisions. You'll have direct input on what we build and how we build it. - **Strong technical leadership:** Our CTO (Priya Patel) built ML infrastructure at Palantir. Technical bar is high and the conversations are substantive. - **A product that needs good infrastructure:** Signal is growing fast — 31% of eligible customers adopted it in 2 weeks. The infrastructure you build will be under real load and real scrutiny from real customers. - **Early equity that means something:** We're pre-Series A. The equity offered at this stage has more upside than post-Series A hires, and the strike price reflects where we are today. - **Quarterly in-person weeks:** The data platform team meets in person once per quarter — SF or wherever makes sense. The rest is fully remote. ## Compensation - Base salary: $165,000–$195,000 (depending on experience) - Equity: 0.15–0.25% (options, 4-year vest with 1-year cliff) - Benefits: full medical/dental/vision, $2,000/year professional development, home office stipend ($500 one-time)
Tips for best results
- 1The first sentence of the role overview is your most important recruiting sentence. Most engineers decide in 10 seconds whether to keep reading — start with what they'll actually be building.
- 2The 'nice to have' section should actually be nice to have. If you genuinely require Kafka experience, it's a requirement. Hiding requirements as preferences wastes candidates' time and the recruiting team's time.
- 3Publishing a salary range increases application quality and reduces time-to-offer. Candidates who self-select based on compensation are better candidates than those who discover misalignment at the offer stage.
- 4Avoid 'we move fast' and 'high growth environment' — every startup says these things and they've lost all meaning. Describe specifically what fast looks like in this role (e.g., 'we ship to production multiple times per day').
- 5The equity section matters more at early-stage companies than most JDs acknowledge. Be specific about percentage ranges — a range of 0.15–0.25% tells a candidate far more than 'competitive equity.'
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