At a Glance
- Tasks: Build and design data architecture from scratch, ensuring quality and scalability.
- Company: Join PACKPack, a people development platform transforming talent into A-Players.
- Benefits: Flexible salary options, stock options, unlimited mentoring, and wellness support.
- Other info: Work in a dynamic environment with opportunities for personal and professional growth.
- Why this job: Be a key player in shaping data strategy and making impactful decisions.
- Qualifications: 4+ years in Data Engineering, strong SQL skills, and a passion for data quality.
The predicted salary is between 60000 - 80000 € per year.
About us
PACKPack is the people development platform for companies building the next generation of talent. We help you transform performers into A‐Players — not with generic training, but with precision coaching, strategic mentorship, and a system that scales.
An international network of elite mentors and operators.
A platform that turns growth into a process, not a guess.
A proprietary methodology rooted in behavioral science and execution.
WHY WE NEED YOUR HELP
We are looking for a Senior Data Analytics Engineer to build our data function from the ground up. We need a builder who can help us forge the strategy leveraging first principle thinking. You will not just be executing tickets; you will be defining the "how" and the "why." You will act as the Architect, Product Manager, and Engineer all in one, ensuring that we build a scalable, trustworthy foundation that drives business decisions.
OUR VALUES
- People‐Focused
- Relentless Growth
- Straightforward Execution
- Founder Mindset
- MAMBA MENTALITY: "Hard work outweighs talent. Mamba mentality is about 4 a.m. workouts, doing more than the next guy, and then trusting in the work you've put in when it's time to perform. Without studying, preparation, and practice, you're leaving the outcome to fate. I don't do fate." - Kobe Bryant
About the role
Primary Responsibilities
- Architecture & Strategy: Design the Stack: You will evaluate our current needs and contribute to design the appropriate data architecture. You will actively participate in defining the right balance between speed and scalability, choosing the tools that fit our stage (whether that is a modern data stack, product analytics, or a hybrid).
- Build from Scratch: You own the "plumbing." You will set up the infrastructure to ingest, transform, and store data from our production systems and third‐party tools.
- Scalability: Design a system that serves us today but won’t break when we scale 10x.
- Data Quality & Validation (The Guardrails): Trust is Everything: You are the gatekeeper. You will implement automated testing and validation rules to ensure that if a metric looks wrong, we know about it before the CEO does.
- Instrumentation Standards: Work with Full Stack engineers to define what we track. You will create the Tracking Plan to ensure data consistency across the platform.
- Metrics Definition & Visualization: Partner with leadership to translate vague business questions into strict mathematical definitions (e.g., Retention, MRR, Activation). Build the "Source of Truth" dashboards that Leadership and Product teams check every morning. Ensure logic is DRY (Don’t Repeat Yourself) and version‐controlled.
Secondary Responsibilities
- Culture & Democratization: Educate the wider team on data literacy. Ensure that the data stack you build is accessible enough for Product Managers and Customer Success to self‐serve answers.
- AI & Future Readiness: Ensure our data structure is clean, organized, and documented enough to support future AI/ML initiatives.
Requirements
- Technical Skills: English level: B2, we are an international team and all meetings and communication are in English.
- Data Architecture: You have a strong opinion on how to structure data for a startup. You understand the trade‐offs between ELT, ETL, and direct querying.
- SQL Mastery: Expert‐level SQL skills. You can manipulate data in any environment (Warehouses or Application DBs).
- Validation Mindset: You are obsessive about data quality. You don’t assume data is clean; you prove it with tests.
- Engineering Best Practices: You treat data code like production software—git, CI/CD, and code reviews are non‐negotiable.
- Programming: Proficiency in Python (or similar) for custom scripts, orchestrations, or data transformations.
Nice to Haves
- Experience with PostHog: Experience setting up, managing, or querying PostHog (including HogQL) is a plus.
- The "0 to 1" Journey: You have set up the very first data stack in a startup before.
- Modern Data Stack: Experience with dbt, Snowflake/BigQuery, and Fivetran.
Soft Skills
- Extreme Autonomy: You are the first data hire. You need to be able to set your own roadmap and execute without daily hand‐holding.
- First Principle Thinking: You break problems down to their core truths rather than reasoning by analogy ("We do this because everyone else does it").
- The Translator: Ability to explain technical trade‐offs to non‐technical stakeholders clearly.
Experience Required
- 4+ years of experience in Data Engineering, Analytics Engineering.
- Proven track record of defining metrics and ensuring data integrity.
- Experience working directly with engineering teams to instrument code.
Compensation
€60.000 - €80,000
Contract: Working hours: full time, Contract term: permanent contract.
We have designed a flexible compensation structure for our developers (and engineers), giving you the choice between two options:
- Option A – Performance-Based Package: Base salary aligned with role and seniority. Bonus up to 50% of your salary based on Company, Team, and Individual performance. There is no cap on bonuses — exceptional performance can lead to higher earnings, linked directly to over‐performance. Quarterly evaluations determine the performance multiplier (based on Deliverables, Skills, and Culture).
- Option B – Fixed-Only Package: Base salary 15‐20% higher than Option A. No participation in the performance‐based bonus system. Annual review: you can switch back to Option A during salary reviews if you wish. Note: The fixed salary in Option B is designed to offer more financial stability for those who prefer predictability, while Option A offers higher upside potential linked to performance.
Benefits
- STOCK‐OPTION PLAN: we have allocated employees a specific number of shares in the company.
- UNLIMITED MENTORING, COACHING & THERAPY SESSIONS: You will have the opportunity to follow a mentoring or a coaching path directly on our platform. Moreover, you will also have access to therapy sessions, thanks to our partner Serenis.
- WELFARE PLATFORM & MEAL VOUCHERS: every month you will receive an amount on the Edenred Application and you will decide how to spend it.
We are an equal opportunity employer and welcome applications from all qualified individuals, regardless of age, gender, ethnicity, sexual orientation, disability, or any other protected characteristic.
Data Analytics Engineer in London employer: Pack
At PACKPack, we pride ourselves on being an exceptional employer that fosters a culture of relentless growth and innovation. As a Senior Data Analytics Engineer, you will not only have the opportunity to build our data function from the ground up but also benefit from unlimited mentoring and coaching sessions, a flexible compensation structure, and a supportive work environment that values autonomy and first principle thinking. Join us in shaping the future of talent development while enjoying a range of benefits designed to enhance your professional journey.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analytics Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, analyses, or any relevant work. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to data analytics. Think about how you would approach building a data stack or ensuring data quality—this is your chance to shine!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Data Analytics Engineer in London
Some tips for your application 🫡
Show Your Passion:When writing your application, let your enthusiasm for data analytics shine through! We want to see how excited you are about building data functions and driving business decisions. Share your journey and what motivates you in this field.
Tailor Your Application:Make sure to customise your application to reflect the specific requirements of the Data Analytics Engineer role. Highlight your experience with data architecture, SQL mastery, and any relevant tools you've used. We love seeing how your skills align with our needs!
Be Clear and Concise:Keep your application straightforward and to the point. Use clear language to explain your past experiences and how they relate to the role. Remember, we appreciate straightforward execution, so avoid jargon and get straight to the facts!
Apply Through Our Website:We encourage you to submit your application directly through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at PACKPack!
How to prepare for a job interview at Pack
✨Know Your Data Architecture
Before the interview, brush up on your understanding of data architecture principles. Be ready to discuss how you would approach designing a scalable data stack and the trade-offs between ELT and ETL. This will show that you can think critically about the role and its responsibilities.
✨Showcase Your SQL Mastery
Prepare to demonstrate your SQL skills during the interview. You might be asked to solve a problem or manipulate data on the spot. Practising common SQL queries and being able to explain your thought process will highlight your expertise and confidence in handling data.
✨Emphasise Your Validation Mindset
Make sure to convey your obsession with data quality. Discuss specific examples where you implemented testing and validation rules to ensure data integrity. This aligns perfectly with the company's need for a gatekeeper who prioritises trust in data.
✨Be Ready to Translate Technical Concepts
Since you'll be working with non-technical stakeholders, practice explaining complex technical concepts in simple terms. Prepare examples of how you've successfully communicated technical trade-offs in previous roles, showcasing your ability to bridge the gap between tech and business.
