Financial Data Engineer

We are seeking an experienced and detail-oriented Financial Data Engineer to build, manage, and scale high-quality financial data products used by investment and research professionals.

The ideal candidate will combine strong financial-domain knowledge with hands-on expertise in Python, SQL, data engineering, and large-scale financial datasets. This role requires a strong focus on data accuracy, process transparency, automation, and end-to-end ownership of the financial data lifecycle.

Key Responsibilities

• Design and implement business logic for financial data products, including actuals, consensus estimates, management guidance, pricing, and valuation datasets.
• Build reliable data pipelines that support downstream platforms such as Excel-based applications, APIs, data feeds, and cloud data warehouses.
• Own the complete data lifecycle, from sourcing and transformation to validation, delivery, and ongoing quality monitoring.
• Work with complex financial datasets derived from public-company filings, sell-side estimates, market data, and corporate actions.
• Apply financial-data concepts such as point-in-time data, security master management, calendarization, restatements, and historical data tracking.
• Design and document transparent, auditable data processes with clear lineage from source to final output.
• Develop automated data-quality frameworks, including validation rules, reconciliation checks, exception monitoring, and statistical anomaly detection.
• Investigate data discrepancies and implement scalable solutions to prevent recurring issues.
• Partner with product, engineering, and business teams to ensure accurate and timely delivery of financial data.
• Communicate complex data logic, financial concepts, and quality issues clearly to technical and non-technical stakeholders.
• Contribute to the continuous improvement of data architecture, processes, controls, and operational standards.

Required Skills and Qualifications

• Bachelor’s or Master’s degree in Financial Engineering, Quantitative Finance, Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline.
• 4-7 years of experience in financial data engineering, quantitative data, capital markets, financial analytics, or a related field.
• Strong understanding of the end-to-end financial data lifecycle.
• Advanced proficiency in Python and SQL.
• Hands-on experience working with large and complex time-series datasets.
• Experience handling fundamental financial data such as reported actuals, estimates, guidance, financial statements, and valuation metrics.
• Familiarity with market data, including security prices, corporate actions, and reference data.
• Strong understanding of data modelling, data transformation, data validation, and reconciliation processes.
• Ability to identify data-quality issues and perform detailed root-cause analysis.
• Strong analytical, problem-solving, documentation, and communication skills.
• Ability to work independently and take ownership of business-critical data products.
• Flexibility to collaborate with global teams and work U.S. business hours when required.

Preferred Qualifications

• CFA, FRM, or CQF certification, completed or in progress.
• Experience working with financial data platforms, capital-markets technology companies, research firms, or investment-management organizations.
• Knowledge of model-risk management or regulatory model-validation frameworks.
• Familiarity with cloud environments such as AWS, Microsoft Azure, or Google Cloud Platform.
• Experience with cloud data warehouses such as Snowflake or BigQuery.
• Exposure to machine-learning techniques used for forecasting, pricing, risk classification, or anomaly detection.
• PhD in a quantitative discipline may be considered in place of some professional experience.

Work Schedule: Monday to Friday (Flexibility to work during US shifts, when required, in a work-from-home setup.)

Work Type : Hybrid