Senior Data Engineer

Falcon Smart IT (FalconSmartIT); Greater London; Hybrid; GBP 70,000 - 90,000; Full time

Job summary

Falcon Smart IT (FalconSmartIT) is seeking a skilled professional for a hybrid position in London focused on architecting and developing data systems. The role demands 12+ years of Python experience, strong AWS knowledge, and familiarity with Snowflake and data stack technologies.

Key responsibilities include developing metadata management processes and ensuring the performance of data pipelines. Ideal candidates will have exposure to tools like Apache Airflow and a background in financial services.

Qualifications

• 12+ years of experience in Python.
• Strong understanding of AWS services like Lambda and ECS.
• Hands-on experience with Snowflake and data stack technologies.

Responsibilities

• Contribute to architectural design and build system integrations.
• Implement and maintain robust data pipelines.
• Develop and manage metadata processes and tools.

Skills

AWS

Snowflake

Python

Tools

Apache Airflow

PostgreSQL

Job description

Job Location: London, UK/Hybrid - 2 days Onsite and 3 Days Remote

Job Type: FTC - 1 Year

Mandatory Skillsets: AWS + Snowflake + Python

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: 12+ years of experience in Python.
• 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.