At a Glance
- Tasks: Build and design data architecture from scratch, ensuring scalability and data quality.
- Company: Join PACK, a people development platform transforming talent into A-Players.
- Benefits: Competitive salary, stock options, unlimited mentoring, and wellness support.
- Other info: Flexible compensation options and a culture of relentless growth.
- Why this job: Be a key player in shaping our 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
PACK 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.
- 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 England employer: Pack
PACK is an exceptional employer that prioritises people development and fosters a culture of relentless growth and straightforward execution. As a Senior Data Analytics Engineer, you will have the unique opportunity to build our data function from the ground up, working in an environment that values autonomy and innovation. With benefits like unlimited mentoring, coaching sessions, and a flexible compensation structure, PACK empowers its employees to thrive both personally and professionally.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analytics Engineer in England
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, analyses, or any relevant work. This gives us a tangible way to see 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, as these are key topics for us at PACK.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in being part of our team and culture.
We think you need these skills to ace Data Analytics Engineer in England
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Data Analytics Engineer. Highlight your experience with data architecture, SQL mastery, and any relevant projects that showcase your skills. We want to see how you can build from scratch!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data and how your first principle thinking aligns with our mission. Show us your personality and how you embody our values.
Showcase Your Technical Skills:Don’t just list your technical skills; demonstrate them! Include specific examples of how you've used SQL, Python, or any other tools in past projects. We love seeing real-world applications of your expertise.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Pack
✨Know Your Data Architecture
Before the interview, brush up on your understanding of data architecture. Be ready to discuss how you would design 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 help you stand out as a candidate.
✨Emphasise Data Quality
Since the role requires a validation mindset, come prepared with examples of how you've ensured data quality in past projects. Discuss specific testing methods you've implemented to catch errors before they impact decision-making.
✨Communicate Like a Pro
As a Data Analytics Engineer, you'll need to translate complex technical concepts to non-technical stakeholders. Practice explaining your previous projects in simple terms, focusing on the 'how' and 'why' behind your decisions. This will demonstrate your ability to bridge the gap between tech and business.
