Analytics Engineer

Note from the Hiring Manager — Maren

I'm excited to welcome an
Analytics Engineer
who will help us scale our data foundations and enable better decision-making across PhantomBuster.

Our data stack is solid, and we're entering a phase where
data quality, shared metrics, and fast, reliable insights
matter more than ever. This role is about building trusted data models, enabling experimentation, and helping teams across Product, Growth, and Revenue use data with confidence.

What makes our Data team special is our focus on pragmatism, ownership, and impact. We work closely with stakeholders, favor simple and robust solutions, and care deeply about making data useful in day-to-day decisions.

If you enjoy working end-to-end - from data modeling to dashboards and stakeholder collaboration - this role offers both autonomy and impact.

TL;DR

We're hiring an
Analytics Engineer
to strengthen our data models, analytics, and experimentation capabilities, and to help teams across the company make better, faster decisions using data.

About PhantomBuster
PhantomBuster is a web automation SaaS that allows businesses to grow faster. We enable thousands of companies to boost their growth by finding and connecting with their ideal customers seamlessly.

Founded in 2016, PhantomBuster developed a toolbox of over 120 flows (Phantoms) to help businesses scale their sales and marketing processes. We allow our users to automate finding and enriching data about their potential customers and leverage that data to connect with them.

Today, we are quickly moving from a catalog of automation tools to an AI-powered Sales coach product. We need to empower and expand our technical teams to ensure this transformation is seamless, fast, and powerful.
About The Role And The Team
The
Data team
at PhantomBuster is responsible for analytics, experimentation, data modeling, and machine learning. We enable teams across the company to make better decisions by providing reliable data, clear metrics, and actionable insights.

You will join a team composed of
two Analytics Engineers, two Machine Learning Engineers, and a Manager
. We operate with a high degree of autonomy while partnering closely with Marketing, Growth, Product, and Revenue teams.

This role focuses on
analytics engineering and product analytics
, with exposure to marketing and growth topics as part of a broader, cross-functional scope.

Our Data Stack

• AWS for the data lake
• Snowflake as the data warehouse
• dbt for transformations and modeling
• Tableau for dashboards and reporting
• Self-serve analytics tools such as Amplitude and ChartMogul
• Web and product tracking tools used where relevant (e.g., Google Analytics, GTM)

What You'll Do

Analytics & insights

• Conduct descriptive and exploratory analyses to support product, growth, and business decisions.
• Build and maintain dashboards and reports to monitor KPIs, experiments, feature launches, and overall performance.
• Partner with Product and Growth teams to analyze user behavior, funnels, and retention.

Data modeling & foundations

• Design and maintain clean, well-documented data models in Snowflake and dbt.
• Improve metric definitions and ensure consistency across teams and dashboards.
• Contribute to the reliability and scalability of our analytics infrastructure.

Experimentation & decision support

• Support experimentation efforts (A/B testing) by helping define metrics, analyze results, and interpret outcomes.
• Help teams frame the right questions and turn data into clear recommendations.

Culture & collaboration

• Communicate insights clearly to non-technical stakeholders.
• Contribute to a strong data-driven culture through documentation, presentations, and workshops.
• Act as a thought partner for teams making strategic or operational decisions.

Who You Are

Experience & mindset

• 3+ years of experience in analytics, analytics engineering, or a similar data role.
• Comfortable working at the intersection of data, marketing, and business.
• Curious about user behavior, growth levers, and how products scale.

Technical skills

• Strong SQL skills and solid experience with Python (or another data language such as R or Scala).
• Hands-on experience with modern data stacks (e.g., Snowflake, dbt, Dataiku).
• Experience with analytics and visualization tools (e.g., Tableau, Looker, Power BI).
• Familiarity with web or product analytics tools (e.g., Amplitude, Google Analytics, Google Tag Manager).

Soft Skills

• Able to explain complex analyses clearly to non-technical audiences.
• Strong ownership mindset: you take responsibility for delivering results and following through, not just producing analyses.
• Comfortable prioritizing your own work and helping stakeholders clarify, scope, and sequence their data requests so the most impactful work gets done first.
• Autonomous, rigorous, and resourceful.
• Collaborative and comfortable working in a fast-growing environment.

Nice to Have

• Experience in a B2B SaaS or product-led growth company.
• Familiarity with marketing attribution models and growth performance analysis.
• Hands-on experience with web and product analytics tools such as Amplitude or Google Analytics.
• Experience designing, analyzing, or supporting experiments (A/B testing), including metric definition, result interpretation, and the use of experimentation platforms such as Amplitude Experiments (or similar tools).
• Exposure to cloud environments (AWS, GCP, Azure).

What Success Looks Like (First 6–12 Months)

• Reliable marketing analytics: clean, trusted tracking across acquisition and activation funnels.
• Actionable insights: dashboards and analyses that directly inform growth and product decisions.
• Stronger decision-making: marketing and product teams confidently using data to run experiments and prioritize work.
• Scalable foundations: improved data models, documentation, and tracking practices that scale with the company.

What's in it for you?

• Fully remote working environment.
• Freedom to make an impact at a small, self-funded, and profitable tech startup.
• Benefits and perks are described below.

Hiring Process

• Screening with our Talent Partner, Jakub (45min).
• Job Fit interview with our Head of Data, Maren (1hr) + a Team Member.
• Remote exercise (one week to do it), followed by a debriefing meeting with a Team Member and Nicolas (1hr).
• Cultural fit interview with two colleagues from another department (1h).

AI Guidelines
At PhantomBuster, we use AI tools daily to build things faster. As the use of AI in recruitment might have multiple implications, we want to be transparent about how we might use it and how we expect you to use it during our recruitment processes.

How we use AI:

• Draft and refine job descriptions and case studies,
• Draft emails during the process,
• Find interview timeslots,
• Summarize interview notes,

How we don't use AI:

• Assess your CV or profile,
• Evaluate interview performance,
• Conduct interviews,
• Grade technical tasks or case studies.

You interact with humans. Period.
We invite you to use AI throughout the recruitment process. However, we want to meet YOU, not machine-generated responses. Your unique perspective matters so much more than perfect AI answers.

Feel free to use AI to:

• Research our company, team, or product,
• Refine your CV, portfolio, or LinkedIn profile,
• Prepare for interviews and brainstorm potential questions,
• Polish your case study or presentation,
• Draft emails to us,

Let us know throughout the process how you used AI
- we are curious to learn.

Don't use AI to:

• Search for answers during interviews (unless we ask),
• Create documents (CV, portfolio, presentation) from scratch without your input,
• Build case studies or technical tests without your personal touch.

If you have any questions, we would love to hear from you →