Senior Data Engineer – Data Foundation, Analytics

Das ist der Job

Responsibilities As CUJU’s first Senior Data Engineer, you will build the data foundation of the company.

Darum lohnt es sich

BigQuery, Snowflake, Databricks) where they add clear value Ensure the architecture scales with product, AI, and data growth without overengineering Optimize pipelines for scalability, cost efficiency, and performance Write clean, maintainable, and well‑structured Python code following software engineering best practices Work closely with Product, Engineering, AI, and Business teams Requirements 5-7+ years of professional experience in data‑heavy roles (data engineering, ML engineering, or similar).

Excellent communication skills in English, with the ability to effectively collaborate with cross‑functional and international teams.

ATS Optimization Keywords Hard Skills data engineering machine learning engineering Python SQL data pipelines data services data modeling data architecture cloud‑native architectures data analysis Soft Skills communication collaboration problem‑solving adaptability decision‑making teamwork organizational skills attention to detail critical thinking passion for sports Industry Keywords data metrics retention performance insights CI/CD monitoring logging fast‑paced environments early‑stage environments real‑world data performance analytics Tools & Technologies AWS S3 Lambda ECS EKS Glue Athena Redshift BigQuery Snowflake #J-18808-Ljbffr Your mission is to transform fragmented data across multiple systems into a reliable, scalable, and usable data layer that enables: Clear business metrics (e.g.

MAU, retention) Product decision‑making Future player performance insights Define and implement core data models (users, events, performance) Design and operate robust, production‑grade data pipelines Establish a single source of truth for key business and product metrics Structure data for business and product analysis (retention, funnels, activation) Build and deploy data services and jobs on AWS (e.g.

S3, Lambda, ECS/EKS, Glue, Athena, Redshift, etc.) Make pragmatic decisions on how data is stored, processed, and accessed Evaluate and introduce tools (e.g. Strong programming skills in Python (clean architecture, testing, modular design not just scripts). Solid SQL skills and experience designing analytical schemas.

Hands‑on experience building production data pipelines and services. Strong experience with AWS and cloud‑native data architectures. Familiarity with infrastructure concepts (CI/CD, monitoring, logging, deployments). Comfortable working with imperfect, real‑world data and evolving requirements.

Experience working in fast‑paced or early‑stage environments. Passionate about sports, performance analytics, and leveraging data to make a real‑world impact.