We are looking for an experienced Data Analyst – EDM to join our team to support the development, implementation, and ongoing maintenance of the firm’s enterprise data management platform. You will work closely with data engineers, data scientists, and business stakeholders to ensure the data platform is meeting the organization’s evolving data and analytical requirements.
Responsibilities
Collaborate with the data engineering team to design and implement data pipelines, ETL/ELT processes, and data quality checks.
Perform in-depth data analysis and modelling to uncover meaningful insights, trends, and patterns from the firm's data assets.
Proactively ensure the process of maintaining data quality and reconciled data is clear and efficient.
Develop interactive dashboards, reports, and visualizations using business intelligence tools like Power BI to support data-driven decision-making.
Identify opportunities to enhance data quality, enrich data sources, and improve the overall usability of the enterprise data platform.
Document data lineage, metadata, and data governance policies to ensure the integrity and reliability of the firm's data.
Provide training and support to business users on how to effectively leverage the data management platform and self-service analytical capabilities.
Collaborate with cross-functional teams to align the data platform with the organization's strategic objectives and regulatory requirements.
Research and evaluate new data management technologies, techniques, and best practices to continuously improve the firm's data capabilities.
Requirements
5+ years relevant experience as a
3+ years in Financial Services industry (fund house or asset management Must)
Desire to learn and understand the business
Proficient in SQL, Python, or other data analyst data manipulation and analysis programming languages
Strong understanding of data modeling, data warehousing, and business intelligence concepts
Hands-on experience with data visualization and reporting tools such as Power BI, Fabric, or other enterprise data tools.
Familiarity with data engineering technologies like Snowflake, DBT, Spark, Kafka, or cloud-based data services.
Ability to translate business requirements into data-driven solutions and communicate insights to stakeholders.
Strong problem-solving, critical thinking, and decision-making skills.
Excellent written and verbal communication skills to collaborate effectively with technical and non-technical teams.
