Data Scientist

The Senior Data Engineer is a highly skilled data management specialist in charge of developing and implementing complex data pipelines and data warehouse solutions. They will work closely with data engineers, architects and product owners to ensure high-quality delivery of Data Assets.

Required Experience:

At least 5 years of experience in Data Engineering or a related field

A minimum of 1 years of hands-on experience with Databricks in a production environment

Strong expertise in cloud platforms (e.g. AWS, Azure, GCP), Data Lake technologies and Data Warehousing

Experience with data ETL tools and BI solutions (e.g., Airflow, PowerBI)

Experience with data quality and governance tools (e.g., Atlan, Monte Carlo)

Solid knowledge of data modelling concepts

Excellent communication and collaboration skills

A problem-solving and analytical mindset

Ability to work autonomously and as part of a team

Key responsibilities & duties include:

Data Engineering Expertise: Proven experience in architecting and delivering large-scale, cloud-native data solutions

Advanced Knowledge in Databricks and Snowflake: Hands-on experience in Databricks, Spark, Pyspark and Delta Lake, with strong skills in data warehousing and lakehouse solutions

MLOps Skills: Practical experience in MLOps, ideally with MLflow for model management and deployment

Cloud Proficiency: Strong knowledge of AWS, with additional experience in Azure advantageous for multi-cloud setups

Programming Proficiency: Advanced coding abilities in Python, SQL and Scala

Tooling Competence: Familiarity with version control (GitHub), CI/CD tools (Azure DevOps, GitHub Actions), orchestration tools (Airflow, Jenkins) and dashboarding tools (Tableau, Alteryx)

Desirable Experience:

Master's in Computer Science, Data Science or a related field

Proven experience in designing, developing and implementing complex data pipelines and data warehouse solutions in an enterprise environment

Experience with Scaled Agile planning and execution