At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. Our Corporate Finance team, part of Finance Data Science and Engineering (DSE), brings this same innovative approach to our financial operations. As trusted partners to our finance stakeholders, we analyze key metrics, surface actionable insights, and drive value for the business. As Netflix continues to grow, the need for advanced analytics in Finance is greater than ever – leveraging cutting-edge techniques to uncover deeper insights and identify new opportunities for impact. You’ll play a key role in shaping the future of how our team builds business-critical revenue data products within Corporate Finance at Netflix, helping unlock new value and drive strategic decision-making through data. Learn more from our blog on how Finance AEs are driving the business forward through data. In this role, you will: - Champion a “data as a product” mindset: Set the standard for robust documentation, testing, and data model design – making revenue data products easy to understand, trust, and extend. - Mentor and uplevel the team: Share best practices in analytics engineering, code quality, and documentation; coach others to adopt product-minded approaches and raise AE standards across the org. - Partner cross-functionally with Finance, Data Science, Data Engineering, and Revenue stakeholders to define the future vision for key revenue data products, surfacing opportunities for innovation and improved business impact. - Continuously improve business-critical analytics workflows for revenue that power Investor Relations reporting and all downstream revenue consumers, ensuring reliability, scalability, and transparency. - Drive root-cause analysis and ask “why” to deeply understand the drivers of revenue data issues, proactively identifying improvements to workflows, code, and models. - Implement and advocate for orchestration best practices to ensure the reliability and timeliness of revenue data pipelines. - Leverage AI tools (e.g., Claude Code, Cursor, MCP servers) to boost developer productivity and workflow efficiency, and share learnings with the team. - Contribute to the adoption of modern AE standards (e.g., WAP—Write Audit Publish) and help define best practices for documentation, testing, and code review. - Work with modern data infrastructure (Iceberg, SQL, orchestration tools), with opportunities to influence adoption of new tools (e.g., DBT, Pandas). Technical Skills: - Demonstrated ability to write clean, readable, and impactful SQL code. - Advanced SQL skills for analytics data processing (orchestration, metrics creation, documentation). - Familiarity with DBT or other semantic data modeling tools for strong data foundations. - Experience with AI tools for automation (e.g., Cursor, Claude, Commands, Rules) is a plus. - Experience with Python Pandas framework is a plus. What Sets You Apart: - 8+ years in analytics/data engineering, ideally in high-stakes, business-critical data environments. - Data-as-a-product mentality and ability to influence and drive cross-functional change from specification, planning, implementation, and beyond launch. - Deep sense of ownership, curiosity, and a drive to get at the “why” behind data and workflow design. - Proven ability to mentor, influence, and raise the bar for technical and product standards within a team. - Excellent communication skills for both technical and non-technical audiences. - Collaborative, innovative, and comfortable navigating ambiguity in a fast-moving environment. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $297,000.00 - $509,400.00. This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Job is open for no less than 7 days and will be removed when the position is filled.

Kit is an email-first operating system for creators who mean business. We help creators grow and monetize their audience with ease. For coaches, YouTubers, authors, podcasters, and other creatives, there isn't a better marketing hub to rely on to grow audiences, automate email marketing, and sell digital products — all within one platform. More importantly, there isn't a team more committed to helping creators earn a living. We're on a mission to help creators earn $1 billion using our creator marketing platform. We have always been 100% independent and 100% remote. We are proud to have built a product that our customers love, and we look for people who have enthusiasm and belief in our mission, vision, and values to join our team. We're also embracing AI thoughtfully — both in how we build and how we hire to ensure our team is adaptable, innovative, and ready for what's next. The role Kit is at an inflection point. We have the product, the customers, and the mission — but our ability to make fast, confident decisions across Finance, Product, Marketing, and Revenue is still bottlenecked by an analytics layer that hasn't kept pace with the business. We're hiring a Staff Analytics Engineer to solve for that: to build the canonical data foundation that lets every team at Kit operate from a single source of truth, and to raise the bar for how data work gets done across the entire function. This is a full-time IC role for someone who thinks in systems, communicates in writing, and finds genuine satisfaction in turning ambiguous metric definitions into reliable, well-documented truth. The success of this role is measured by whether the rest of the company can confidently explore data without depending on the Data team for routine interpretation. Your support system You’ll report to Samuel Umachi, Head of Data, and collaborate closely with an analytic engineer, infrastructure engineers, and cross-functional partners in Product, Engineering, Finance, Marketing, Sales, and Creator Growth. You’ll partner directly with stakeholders who rely on the Reporting Hubs you’ll help build and maintain. What you'll do - First Week: Complete onboarding in Notion and meet your teammates through Get-To-Know-You calls. Get oriented in our core tools: dbt, Github, Redshift, Omni, Slack, and Linear. Start reading existing documentation on our Reporting Hub architecture and canonical metric definitions. - First Month: Audit active Reporting Hub models across Finance, Marketing, Sales, Product Strategy, and Creator Lifecycle. Map current churn logic implementation and identify inconsistencies in canonical metric definitions. Assess Redshift performance strain areas and flag fragile model dependencies. Publish a written architectural assessment memo with your initial findings and recommended priorities. Serve as the data team’s voice in cross-functional discussions about metric definitions, attribution logic, and analytical rigor across Finance, Product, and Marketing. - First Six Months: Propose and begin executing a 6–12 month modernization roadmap for our modeling layer. Refactor the highest-risk models across all business verticals Clarify attribution model contracts and improve cross-functional documentation standards. Measurably reduce warehouse inefficiencies through distribution and sort key optimization, not infrastructure scaling. Improve Segment event modeling structure and align event design with reporting needs in partnership with Product and Engineering. Drive company-wide adoption of canonical metrics by working directly with functional leads to replace ad hoc definitions with documented, auditable standards. - First Year: Complete the Reporting Hub foundation across business verticals with canonical metric consistency enforced. Enable true stakeholder self-serve, so teams can explore data without relying on the Data team for routine questions. Reduce model rework caused by upstream ambiguity through upstream design patterns and documentation. Raise the overall modeling sophistication of the Data team through mentorship, standards, and shared tooling. Leadership makes resource allocation and growth decisions using a shared metric layer they trust without qualification. Finance closes faster because revenue and subscription models are reliable and consistent. Product and the broader company make decisions with cohort and performance data they no longer need to manually verify, allowing the data team to ship faster with less rework. What will S.E.T. you up for success Skills - Deep SQL mastery. You write queries that are readable, performant, and designed for maintainability. You understand relational semantics deeply, including join selection, cardinality management, and predicate placement. - Redshift expertise. You know how sort keys, distribution styles, concurrency, vacuum, and analyze behavior work, and how to design around their constraints. - dbt fluency. You build modular, well-documented, refactorable dbt code. You have opinions about macro design, test strategy, and what “good” looks like in a dbt project at scale. - Business modeling strength. You can translate revenue recognition logic, attribution frameworks, funnel stages, and lifecycle metrics into reliable, auditable models that stakeholders trust. - Written communication. You write clear architectural memos, modeling documentation, and decision rationale. You communicate trade-offs, not just conclusions. - Analytical judgment. You have experience navigating contested metric definitions, challenging dashboard results, or attribution debates while maintaining analytical rigor. - Stakeholder translation. You can explain complex metrics and modeling decisions to different stakeholders and communicate in terms they trust. Experiences - 7–10+ years working in analytics engineering, data engineering, or a closely related role, with at least a few years operating at a senior or staff level. - Designed or significantly refactored a warehouse modeling layer in a production environment. - Built or stewarded canonical metric definitions in a multi-stakeholder environment. - Worked in a SaaS company where revenue recognition, churn logic, or subscription modeling was a core part of the work. - Experience with product analytics modeling, Segment event architecture, identity resolution, or cohort/retention modeling is a strong plus. - Collaborated with infrastructure engineers to address performance challenges at the warehouse level. Traits - As a staff level IC, you see your role as amplifying the effectiveness of the entire data team and driving company wide impact. You lead by example. - You think in systems. When you look at a modeling problem, you see the upstream causes and downstream consequences, not just the immediate fix. - You’re patient with complexity. You don’t oversimplify hard problems, and you don’t ship a definition you can’t defend. - You document as you go, not as an afterthought. You believe that work that’s not written down didn’t really happen. - You push back when necessary. You’re comfortable disagreeing with stakeholders, explaining your reasoning, and holding the line on rigor when speed would create long-term problems. - You use AI tools actively. You’re comfortable using AI to accelerate drafting, exploration, and deployment, while applying your own judgment to the outputs. - You operate with extreme autonomy. At a Staff level, you don’t wait for a detailed brief. You identify what needs to be done, communicate your plan, and move. What won't set you up for success - Defaulting to infrastructure scaling when the real problem is modeling design. - Preferring to build tools or processes before understanding the business context they’re meant to serve. - Optimizing in isolation, without documenting decisions, communicating trade-offs, or looping in collaborators. - Discomfort with ambiguity. Many of our metric definitions are still evolving. You’ll need to lead the process of resolving them, not wait for resolution before acting. - A preference for being told what to build rather than helping define what should be built. Working hours and location We’re 100% remote with team members across the globe. We work mostly on East Coast time, so if you’re based on the West Coast of the United States, Europe, or Asia, you might have a few early mornings or late evenings. We do our best to keep things reasonable, but a little time zone juggling comes with the territory. Compensation + perks + benefits Kit has standardized salaries based on position, no matter where you live. For this role, we’re hiring at our level 5 ($198,000). Level is determined based on experience and our interview process. Perks + benefits include: - Profit Sharing - Kit equity - 401k with a 5% match - We cover up to $2,100 per month toward medical premiums, with dental and vision premiums fully covered. We offer Health Insurance plans through Aetna - $2,000 equipment allowance for your first two years, $1,000 budget every following two years. Company-provided laptops are issued to every Kit team member and are not included in the equipment budget - Individual learning + development budget ($3,500/year) - Gender affirming benefits - Childcare benefit up to $3,000 annually - Twenty (20) days of paid time off during each year of employment - Paid paid vacation: An after-tax bonus of $1,000 for taking five consecutive days of vacation where you’re fully unplugged from work - Ten (10) paid holidays a year - Two weeks of paid sick time each year, including mental health + well being days - Twelve (12) weeks paid parental leave and flexible scheduling in your child’s first year - Up to six weeks of paid bereavement leave, medical leave, and disaster after six months of employment, two weeks of each paid leave in your first six months - Winter Break Closure: Kit closes for a week at the end of December, giving everyone a collective break to enjoy the holiday season. Essential support services remain available, with teams coordinating to ensure coverage during this period - Four-week, paid sabbatical after five years with the team - Fantastic in-person or virtual retreats with the team twice a year How to apply We usually receive a large number of applications, and our goal is to follow up with you within two weeks. You’ll hear from us either way. Here’s what you can expect from the rest of our hiring process: - A phone screen with our recruiter - Hiring manager interview - Take-home assignment / Architectural case study - Technical interview - Final alignment call We’ll keep you informed through every step. To get started, complete this application, including a cover letter and answers to the questions on the next page. We read every single application, and your cover letter and responses help put your experience into context. Kit is an equal opportunity employer We value diversity in all of its forms. Research tells us that applicants who are female or non-binary, as well as applicants who are people of color, are less likely to apply for roles they do not feel 100% qualified for. If you think you meet more than 50% of our requirements but fewer than 100% of them, please apply. We’re imperfect communicators, so think of our job postings as the starting point for discussion rather than proof that you shouldn’t apply. Kit does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.

• Complete onboarding in Notion and meet your teammates through Get-To-Know-You calls. Get oriented in our core tools: dbt, Github, Redshift, Omni, Slack, and Linear. Start reading existing documentation on our Reporting Hub architecture and canonical metric definitions. • Audit active Reporting Hub models across Finance, Marketing, Sales, Product Strategy, and Creator Lifecycle. Map current churn logic implementation and identify inconsistencies in canonical metric definitions. Assess Redshift performance strain areas and flag fragile model dependencies. Publish a written architectural assessment memo with your initial findings and recommended priorities. Serve as the data team’s voice in cross-functional discussions about metric definitions, attribution logic, and analytical rigor across Finance, Product, and Marketing. • Propose and begin executing a 6–12 month modernization roadmap for our modeling layer. Refactor the highest-risk models across all business verticals. Clarify attribution model contracts and improve cross-functional documentation standards. Measurably reduce warehouse inefficiencies through distribution and sort key optimization, not infrastructure scaling. Improve Segment event modeling structure and align event design with reporting needs in partnership with Product and Engineering. Drive company-wide adoption of canonical metrics by working directly with functional leads to replace ad hoc definitions with documented, auditable standards. • Complete the Reporting Hub foundation across business verticals with canonical metric consistency enforced. Enable true stakeholder self-serve, so teams can explore data without relying on the Data team for routine questions. Reduce model rework caused by upstream ambiguity through upstream design patterns and documentation. Raise the overall modeling sophistication of the Data team through mentorship, standards, and shared tooling. Leadership makes resource allocation and growth decisions using a shared metric layer they trust without qualification. Finance closes faster because revenue and subscription models are reliable and consistent. Product and the broader company make decisions with cohort and performance data they no longer need to manually verify, allowing the data team to ship faster with less rework.

• Design, build, and maintain ETL/ELT pipelines across our data infrastructure (BigQuery, Redshift, PostgreSQL) • Develop and own dashboards and reports in Tableau to track key marketing and product metrics • Analyze user behavior, conversion funnels, cohorts, and channel performance to surface growth opportunities • Write complex SQL queries for data extraction, transformation, and ad hoc analysis across multiple database environments • Collaborate with marketing and product teams to define KPIs and translate business questions into data solutions • Ensure data quality, consistency, and reliability across our BI ecosystem • Support A/B testing and experimentation frameworks — from setup through statistical analysis and reporting • Contribute to improving data documentation, governance practices, and internal tooling.