Overview
Build the data pipelines, models, and dashboards that turn raw market and operational data into actionable insight.
Key Responsibilities
• Design and implement secure, performant data pipelines to aggregate, cleanse, and transform data from internal and third-party sources.
• Build and maintain analytics data models and data Mart supporting reporting, dashboards, and downstream consumers.
• Develop interactive dashboards and visualisations that surface trends, investment patterns, and company-level insight.
• Implement data quality controls, validation routines, and lineage tracking to meet regulatory-grade reliability standards.
• Integrate with market data provider APIs for ingestion, reconciliation, and profile enrichment.
• Optimise query performance and storage across relational, columnar, and analytical workloads.
• Partner with business analysts and stakeholders to translate analytical questions into reusable datasets.
• Document data contracts, ETL flows, and operational runbooks.
Key Requirements (Must-Have)
• 5+ years of hands-on data engineering /analytics development experience.
• Strong SQL across at least one of PostgreSQL, SQL Server, Oracle, or Snowflake.
• Proficiency in Python (pandas, PySpark, SQLAlchemy) for data transformation and automation.
• Experience building ETL / ELT pipelines using Airflow, dbt, Informatica, or equivalent.
• Proven delivery on Tableau, Power BI, or Qlik— including parameterised dashboards and row-level security.
• Dimensional / star schema modelling experience and comfort with slowly changing dimensions.
• Experience with REST API integration for data ingestion.
• Version control (Git) and CI/CD for analytics code.
• Able to work Singapore-based, on-site at client premises a minimum of three (3) days per week.
• Eligible to undergo and pass security clearance /background screening for financial sector engagements.
Nice-to-Have
• Familiarity with market data platforms (e.g.,PitchBook, Tracxn, Bloomberg, Refinitiv) and financial data schemas.
• Experience with cloud data warehouses (BigQuery, Redshift, Synapse).
• Exposure to streaming data (Kafka, Kinesis) and CDC tooling.
• Working knowledge of machine learning pipelines or basic predictive analytics.
• Tableau Desktop Specialist, Azure Data Engineer, or AWS Data Analytics certifications.
• Bachelor's degree in Computer Science, Data Science, Statistics, or a related quantitative discipline.