Mid/Senior Data Engineer

Responsibilities

• Build a centralized data lake on GCP data services by integrating diverse data sources throughout the enterprise.
• Develop, maintain, and optimize Spark-powered batch and streaming data processing pipelines. Leverage GCP data services for complex data engineering tasks and ensure smooth integration with other platform components.
• Design and implement data validation and quality checks to ensure accuracy, completeness and consistency across our data workflows.
• Work closely with the AI / ML team to enable AI use cases and in particular address data needs for Machine Learning model development and monitoring.
• Collaborate with cross-functional teams, including Data Analysts and other business users from Operations, Marketing and Commercial departments, to extract valuable insights and enable data-driven decision making.
• Collaborate with the Product teams to design, implement, and maintain the data models for analytical use cases.
• Engage in technology exploration and research, the development of PoC s, conducting deep investigations.
• Design and manage ETL/ELT processes, ensuring data integrity, availability, and performance.
• Troubleshoot data issues and conduct root cause analysis when reporting data is in question.
• Work on Gen AI related initiatives aligned with the company goals.

Required Technical Skills

• PySpark - Batch and Streaming
• GCP - Dataproc, Dataflow, DataStream, Dataplex, Pub/Sub, BigQuery and Cloud Storage
• NoSQL (preferably MongoDB)
• Programming languages: Scala/Python
• Great Expectation, or similar DQ framework
• Familiarity with workflow management tools like: Airflow, Prefect or Luigi
• Understanding of Data Governance, Data Warehousing and Data Modelling
• Good SQL knowledge

Business

• Able to communicate effectively, distill technical knowledge into digestible messages in a succinct / visual way
• Proactively identify and contribute with team development initiatives, and supporting junior members

Good to have skills

• • Infrastructure-as-Code, preferably Terraform
  • Docker and Kubernetes
  • Looker
  • AI / ML engineering knowledge
  • Lineage, or relevant tools e.g. Atlan