Summary
✨ AI‑Generated
A senior data engineering role focused on designing data platforms, developing scalable pipelines, managing metadata, and ensuring data quality using modern cloud and analytics technologies.
Highlights
Opportunity to architect modern data platforms, build robust pipelines, and work with advanced cloud and data technologies.
Description
Mandatory Skillsets: Python, Snowflake, dbt, dremio, glue jobs, Apache iceberg, AWS ,sql
Key Responsibilities:
• Architect and Develop: Contribute to the platform’s architectural design and build integration, modelling, data persistence, and analytical systems.
• Data Pipelines: Implement, maintain, and test robust data pipelines.
• Metadata Management: Develop and manage metadata processes and tools.
• Performance Monitoring: Ensure the stability and performance of data pipelines.
• Data Quality: Implement tools for data curation, metadata management, and quality assurance.
• Collaboration: Engage with business and technology teams to align the platform with organizational goals.
Preferred Technical Skills:
• Programming: 5+ years of experience in Python/Java.
• Cloud Expertise: Strong understanding of AWS services (e.g., Lambda, Step Functions, ECS).
• Data Platforms: Hands-on experience with Snowflake and data stack technologies like Apache Iceberg and Spark.
• Workflow Orchestration: Exposure to tools like Apache Airflow, Prefect, Dagster, or DBT.
• Data Services: Familiarity with AWS Glue, Lake Formation, EMR, EventBridge, Athena, and similar services.
• Metadata Tools: Experience with tools like Amundsen, Atlas, DataHub, OpenDataDiscovery, or Marquez.
• RDBMS: Knowledge of PostgreSQL is a plus.
• Industry Experience: Proven experience building enterprise-wide data and analytics systems, preferably in financial services or asset management.
