Head of Data Management

Our client is a data-driven, world-leading trading firm that has been seeing unprecedented success.

They are looking to establish a new data management/operations team and are seeking their first hire to spearhead this initiative.

This will encompass governance, lineage, data quality and everything that modern data pipelines are trying to institute.

A lot of that will be driven out of an ELT process, and rather large transformations with that data quality come into play. The amount of data is growing rapidly within the business; everything they run right now is in AWS, with most of their batch processing going through airflow data stores, everything from S3, Postgres, and time series. For real-time data, they use Kafka.

Priority one is data quality, and how do they measure that? Everything from basic checks of row counts day over day and analysing files to more business logic, so very simple formatting of a location or does this location fit to define values to more complex scenarios where they try to detect anomalies via machine learning.

There is also a lot of transaction data, including trading data, invoicing data, and time-series pricing history data.

Hence, the Head of Data Management will join their dynamic team and contribute to their data management and quality initiatives.

The ideal candidate will have a strong background in data management, data quality, and data governance, focusing on building and maintaining modern data pipelines. This role involves working closely with various teams to ensure data integrity, reliability, and accessibility across the organization.

Key Responsibilities:

• Develop and implement data governance and quality frameworks, focusing on data lineage, profiling, and cataloguing.
• Design and maintain ETL/ELT processes to ensure efficient data movement and transformation across systems.
• Implement data quality checks, anomaly detection, and validation processes using tools such as Soda, Great Expectations, and Monte Carlo.
• Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and ensure data accuracy and consistency.
• Integrate data quality checks within data pipelines using tools like Airflow and Kafka.
• Manage and monitor data workflows, ensuring timely and accurate data processing.
• Develop dashboards and reports to visualize data quality metrics and trends for management.
• Provide technical support and guidance to the data engineering team on best practices for data management and data quality.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.
• Minimum of 5 years of experience in data management, data quality, and data governance.
• Strong experience with modern data quality tools such as Soda, Great Expectations, or Monte Carlo.
• Proficiency in ETL/ELT processes and tools.
• Experience with cloud-based data platforms, preferably AWS
• Experience with data cataloguing tools.
• Strong communication and interpersonal skills, with the ability to work effectively with cross-functional teams.

Preferred Qualifications:

• Experience with financial trading data, including pricing data.