Job description:
The client designs, develops, and maintains the metadata backbone of their next-generation data mesh and platform. They are an autonomous, self-organized team with an agile way of working. All team members participate in defining our short
- and long-term goals, with everyone's opinion being valued during our planning. They acknowledge and address tech debt early and often, before it becomes a larger problem.
Together, they create a challenging but happy work environment, where they try to put you in control of your activities, and where you always have the opportunity to grow both personally and professionally.
Desired knowledge, experience, competence, skills etc You are excited about "connecting the dots" and delivering valuable products within a complex and huge data ecosystem and landscape. You understand that within a large organization like the client, you will have to adapt to, and work with, both old and new technologies running in varied environments.
You have a strong belief in automation, and also you are not afraid of rolling up your sleeves and confirm your hypothesis manually. You take your time with discovery and exploration before writing the first lines of code and validate the value of your proposals through proof-of-concepts and close collaboration with our stakeholders, data architects, and product teams.
You consider well-written documentation as a feature and understand the value of maintaining documentation of the highest quality. You are eager to learn and share your knowledge with your team, and not hesitate to jump into an issue where you believe you can be of assistance.
Requirements:
\-Highly proficient in modern development practices and infrastructure deployment (DevOps) i.e. Git, Terraform, Docker, CI/CD (Github Actions).
\-Extensive experience with cloud platforms such as GCP (preferred), including data storage, processing, and analysis.
\-Extensive experience with data integration and ETL (Extract, Transform, Load) processes, including designing and implementing complex data pipelines (dbt).
\-Good knowledge of the programming languages Python and SQL.
\-Strong understanding of database systems, including relational (Postgres), graph, and vector databases, and understanding of database query performance tuning and optimization.
\-Deep understanding of data warehouses like BigQuery, specifically in designing and implementing data models for efficient data storage and retrieval.Knowledge of data security and privacy principles, including compliance with data governance and regulatory requirements.
\-Excellent problem-solving and troubleshooting skills, with the ability to identify and resolve complex data-related issues.
\-Effective communication and collaboration skills, with the ability to work closely with cross-functional teams and stakeholders to understand and address data requirements and challenges.
Good to have:
Knowledge of machine learning concepts and algorithms, enabling the integration of predictive, semantic and analytical models into data engineering workflows.
Requirements:
\-Highly proficient in modern development practices and infrastructure deployment (DevOps) i.e. Git, Terraform, Docker, CI/CD (Github Actions).
\-Extensive experience with cloud platforms such as GCP (preferred), including data storage, processing, and analysis.
\-Extensive experience with data integration and ETL (Extract, Transform, Load) processes, including designing and implementing complex data pipelines (dbt).
Start: 03/2024
End: 08/2024
Location: Sweden /remote
More info tiina.hapuoja@witted.com