Data Engineer (Mid/Senior)
Key Skills for This Role
Full Job Posting
What you’ll do
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 data's accuracy, completeness, and consistency as it flows through the pipelines.
Work with the Data Science and Machine Learning teams to engage in advanced analytics.
Collaborate with cross-functional teams, including data analysts, business users, and operational and marketing teams, to extract insights and value from data.
Collaborate with the product team to design, implement, and maintain the data models for analytical use cases.
Design, develop, and upkeep data dashboards for various teams using Looker Studio.
Engage in technology explorations, research and development, POC’s, and conduct deep investigations and troubleshooting.
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.
Required Technical Skills
PySpark
GCP - Big Query, Dataproc, Dataflow, Dataplex, Pub-Sub and Cloud Storage
Advanced SQL knowledge
NoSQL (Preferably MongoDB)
Programming languages - Scala/Python
Great Expectation - similar DQ framework
Familiarity with workflow management tools like Airflow, Prefect or Luigi.
Understanding of Data Governance, DWH, and Data Modelling
Good to have skills
Infrastructure as Code - Terraform
Docker and Kubernetes
Looker Studio
AI and ML engineering knowledge
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 data's accuracy, completeness, and consistency as it flows through the pipelines.
- Work with the Data Science and Machine Learning teams to engage in advanced analytics.
- Collaborate with cross-functional teams, including data analysts, business users, and operational and marketing teams, to extract insights and value from data.
- Collaborate with the product team to design, implement, and maintain the data models for analytical use cases.
- Design, develop, and upkeep data dashboards for various teams using Looker Studio.
- Engage in technology explorations, research and development, POC’s, and conduct deep investigations and troubleshooting.
- 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.
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