Data Analytics Engineer

About us

PACK

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

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 forging the strategy leveraging a 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.

• People‑Focused
• Relentless Growth
• Straightforward Execution
• Founder Mindset

"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

• 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.

• 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.

• 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.

• 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.

• Ensure our data structure is clean, organized, and documented enough to support future AI/ML initiatives.

• 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.

• Experience with Post Hog: Experience setting up, managing, or querying Post Hog (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/Big Query, and Fivetran.

• 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…