Data Engineer Lead

3 г. назад

ArgentinaLATAMLeadRemote

big datamachine learning

Lead data engineering role at a startup focusing on Big Data and Machine Learning products.

🚀 Join Our Data Products and Machine Learning Development Remote Startup! 🚀   is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.   We’re looking for a Data Engineer Lead to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect! 🐶🚀

Partner with cross-functional teams to define BI and analytics requirements
Innovate with new tools to improve Mutt Data's infrastructure and processes.
Design dimensional data models and build scalable data pipelines
Develop data models to feed dashboards for teams across the org, as well as client-facing analytics
Develop tools for the team and assist in tech migrations and model design.
Build scalable, high-performance data architectures.
Focus on code quality—review, document, test, and integrate CI/CD.

Experience with stream processing tools like Kafka Streams, Kinesis, or Spark.
Knowledge of Kubernetes.
Solid command of English for understanding and communicating technical concepts (Design Documents, etc.).

Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
Amazon Web Services
Astronomer
Databricks
📊 We are Data Nerds
🤗 We are Open Team Players
🚀 We Take Ownership
🌟 We Have a Positive Mindset
Experience leading and mentoring teams in Data projects, guiding technical decisions, and fostering team growth.
Proven ability to collaborate with stakeholders, translate business needs into technical solutions, and align priorities across teams.
Experience in Data Engineering, including building and optimizing data pipelines.
Strong knowledge of SQL and Python (Pandas, Numpy, Jupyter).
Experience working with any cloud (AWS, GCP, Azure or DBT).
Knowledge of Docker.
Experience with orchestration tools like Airflow or Prefect.
Familiarity with ETL processes and automation.