Job brief

We’re seeking a Senior Data Engineer to enhance our Data Science Team, focusing on implementing and managing data workflows that support machine learning models and large-scale analytics. This role involves designing ETL processes, ensuring data quality, and deploying ML models to production.
The ideal candidate will have a strong computer science background, advanced Python knowledge, and experience with SQL/NoSQL databases, and Docker/Kubernetes. 
You’ll work closely with our data science, business and product teams to drive insights and innovations.


Responsibilities

• Design and implement ETL processes for data transformation and preparation,
• Deploy machine learning models to production environments,
• Manage data pipelines for analytics and operational use,
• Ensure data accuracy and integrity across multiple sources and systems,
• Collaborate with data scientists to support ML algorithms and analytics.

Requirements and skills

• 4+ years of experience in data engineering within a production environment,
• Design and work with big data frameworks (PySpark, Airflow, kafka),
• Design, build and maintain ETL pipelines,
• Writing effective and scalable Python codes,
• Produce clean, consistent, logical code based on designs; submit to GitLab repositoryGenerating infrastructure that allows big data to be accessed and analyzed,
• Refactoring existing frameworks, functions and paradigms to optimize their functioning,
• Ensuring data quality,
• Create and maintain various database systems (MySQL, Oracle, MS SQL Server, Clickhouse),
• Experience with Oracle ODI,
• Provide knowledge transfer to team members and support staff through application demos, walkthroughs, and documentation,
• Experience with ETL process and pipelines,
• Strong proficiency in SQL and experience with relational databases and NoSQL databases like MongoDB,
• Confident with version control systems (GitHub, GitLab, etc.),
• Experience with pandas, numpy, sklearn or other data science frameworks,
• Advanced knowledge of application, data, and infrastructure architecture disciplines,
• Knowledge of data warehousing concepts and experience with data modeling and schema design,
• Problem Solving and communication skills,
• Detail Oriented and decision making.