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Senior Data Engineer

Infinityquest — United Kingdom · Posted ~13 hours ago

Senior Full-time

Skills

Python Snowflake dbt Dremio AWS Glue Apache Iceberg AWS SQL Data pipeline development Data architecture Metadata management Data quality Apache Spark

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Summary ✨ AI‑Generated

Join a data engineering team responsible for architecting and developing integration, modeling, persistence, and analytics capabilities. You will build and maintain robust pipelines, manage metadata and data quality, monitor performance, and collaborate with business and technology stakeholders. Strong Python, SQL, cloud, and modern data-platform expertise is required.

Highlights

Senior data engineering role covering platform architecture, robust data pipelines, metadata and data quality, performance monitoring, and collaboration across business and technology teams.

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.