SENIOR ANALYTICS ENGINEER

Role & Responsibilities

• Analyse large datasets related to payment processing, card issuance, and customer transactions to identify trends, patterns, and insights.
• Use statistical techniques and data mining tools to interpret data and generate actionable insights.
• Develop and maintain dashboards and reports to visualize data and track key performance indicators (KPIs).
• Work closely with business units, product managers, and other teams to understand data requirements and business objectives.
• Provide analytical support to stakeholders by delivering data-driven recommendations and insights.
• Communicate findings and insights effectively through presentations, reports, and visualizations.
• Develop, maintain, and optimize data pipelines with a focus on using dbt for data transformations, ensuring clean, reliable, and accessible data for analysis.
• Ensure the accuracy, completeness, and consistency of data used for analysis.
• Work with data engineers to implement data validation and cleansing processes to maintain high data quality.
• Create and maintain scalable data models in Snowflake using dbt, following best practices in chosen modelling technique.
• Identify opportunities for process improvements and optimization within payment processing and card issuance operations.
• Analyse operational workflows to recommend efficiency improvements.
• Monitor system performance and data trends to proactively address potential issues.
• Potentially contribute in ETL processes using dbt and orchestration tools (e.g., Apache Airflow), optimizing data workflows and performance.
• Ensure data analysis practices comply with industry standards, regulatory requirements, and security protocols.
• Assist in preparing data and reports for regulatory audits and compliance reviews.
• Proactively identify opportunities for process improvements and implement innovative solutions.
  

Preferred Candidate Profile

• Proven experience as a Data Analyst/Analytics Engineer role
• Proficiency in building and managing data transformations with dbt, with experience in optimizing complex transformations and documentation.
• Hands-on experience with Snowflake as a primary data warehouse, including knowledge of performance optimization, data modeling, and query tuning.
• Strong proficiency in data analysis tools and languages (e.g., SQL, Python).
• Strong understanding of data modeling principles and experience applying modeling techniques.
• Proficiencywith data visualization tools such as Tableau, Power BI, or similar.
• Knowledge of payment processing system, card issuance, and related services.
• Experience with cloud-based data solutions (e.g., AWS, Azure, Google Cloud).
• Familiarity with modern data architecture such as data Lakehouse.
• Strong analytical, problem-solving, and communication skills.
• Attention to detail and a commitment to data quality and integrity.
• Familiarity with regulatory requirements and security standards in the financial industry.