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Why Harvey At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. Role Overview We’re looking for a versatile Senior Analytics Engineer to partner closely with our Finance team in building the financial data foundation that drives decision-making at Harvey. With product-market fit already proven and demand surging across diverse customer segments, you’ll design clean, reliable pipelines and semantic data models that turn source data into usable insights. As an Analytics Engineer on our team, you’ll help evolve our data stack, champion best practices in testing and documentation, and collaborate closely with product, finance, and leadership to ensure every team can answer its own questions with confidence. If you combine engineering rigor with a love of storytelling through data we’d love to meet you. What You'll Do Design and build scalable data models and pipelines using dbt to transform raw data into clean, reliable assets that power company-wide financial analytics and decision-making. Define and implement a robust semantic layer (e.g. LookML/Omni/Other) that standardizes financial and operating metrics, including revenue, retention, customer growth, usage, margin, and forecast inputs. Partner cross-functionally with Product, Finance, and the Exec Team to deliver intuitive, consistent dashboards and analytical tools that surface business health metrics (ARR, NRR). Establish and champion data modeling standards and best practices, guiding the organization in how to model data for accuracy, performance, usability, and long-term maintainability. Lead data governance initiatives, ensuring high standards of data quality, consistency, documentation, and access control across the analytics ecosystem. Structure financial metric definitions, business logic, and accounting context in ways that can support AI-assisted reporting, natural language analytics, and automated anomaly detection. What You Have 5+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field. Deep expertise in SQL, dbt, Python, Snowflake. Experience with modern BI tools like (Looker/Omni, or similar). Skilled at defining core financial and operating metrics, uncovering insights, and resolving data inconsistencies across complex systems. Strong familiarity with version control (GitHub), CI/CD, and modern development workflows. Bias for action – you prefer launching usable, iterative data models that deliver immediate value over waiting for perfect solutions. Strong communicator who can build trusted partnerships across Finance, GTM, Product, and Exec stakeholders. Comfortable working through ambiguity in fast-moving, cross-functional environments. Balances big-picture thinking with precision in execution — knowing when to sweat the details and when to move quickly. Experience modeling financial, billing, subscription, CRM, or usage-based revenue data. Strong understanding of business metrics such as ARR, MRR, churn, retention, expansion, bookings, billings, and revenue recognition. Bonus Early employee at a hyper-growth startup Experience with or knowledge of AI and LLMs Data Engineering Experience Experience managing data warehouse (preferably Snowflake) Experience at world-class enterprise orgs (ex: Brex, Ramp, Stripe, Palantir) Compensation $155,000 - $235,000 USD Depending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices [ here ]. Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.