Lead Analytics Engineer

Kit

10d•Remote

About The Position

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 Lead 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.

Requirements

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.
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.
As a lead 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.

Nice To Haves

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.

Responsibilities

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.

Benefits

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