About The Role
• Responsible for creating and maintaining the knowledge materials for the scope of the team, regarding delivery on MSP customers.
• Act as a trusted advisor to customers, developing a deep understanding of their technical challenges and leveraging GCP technologies, patterns and practices to address them effectively.
• Work closely with the delivery teams, Google, and customer engineering teams to:
• Understand and use repeatable and consistent technology stacks across our customer projects.
• Help document and implement managed service processes that are aligned with a project's technology stack, and customer requirements.
• Follow & improve if applicable the best practices, processes and procedures relevant to the team’s activity.
• Ability and willingness to upskill and mentor colleagues, in order to ensure a smooth delivery across teams.
• Possibility to represent Qodea in the relationship with Google or customers. Availability for potential customer visits based on the needs.
• Handle customer escalations and facilitate any patches or fixes as needed to resolve any issues relating to their data platform.
• Investigate issues in and maintain data models to support analytics and reporting
• Monitor and maintain data infrastructure to ensure availability, correctness and performance
• Perform regular checks to ensure data pipelines and ML models are working correctly.
• You’ll collaborate with the customer on a quarterly basis to ensure all requirements continue to be met.
• Scope and plan implementation of any customer changes/requirements
• Maintain automated data pipelines to support data ingestion, ETL, and storage
What Success Looks Like
• Experience with Google Cloud Platform (GCP) or other major cloud providers
• Strong experience in Python with demonstrable experience in developing and maintaining data pipelines and automating data workflows..
• Proficiency in SQL, particularly BigQuery SQL for querying and manipulating large datasets.
• Experience with version control systems (e.g., Git).
• Strong expertise in Python, with a particular focus on libraries and tools commonly used in data engineering, such as Pandas, NumPy, Apache Airflow.
• Experience with data pipelines, ELT/ETL processes, and data wrangling.
• Dashboard analytics (PowerBI, Looker [Studio] or Tableau) experience
• Worked in a client facing role previously and ability to communicate and translate business requirements into technical specifications.
• Collaborative, proactive, logical, methodical, and attentive to detail
• Willingness to mentor and coordinate more junior members of the team.
• A can do attitude and team player
• Excellent English, written and verbal