Senior Data Quality Assurance Engineer

2 Minutes ago • All levels • Data Analysis

Must have:

Statistical Expertise: Advanced knowledge of statistical methods, including linear/non-linear modelling, hypothesis testing, and Bayesian techniques.
AI/ML Integration: Strong skills in applying AI/ML algorithms (e.g., neural networks, random forest, anomaly detection) for data quality checks and predictive analysis. Experience with cloud-based environments (AWS, Azure, etc.).
Quantitative Finance: Deep understanding of financial instruments, market data, and the use of quantitative methods in portfolio management and risk analysis.
Programming Skills: Proficiency in statistical programming languages (Python, R, SQL) and experience with tools like MATLAB, SAS, or similar platforms.
Automation: Experience in developing and implementing automated data validation processes, including real-time monitoring and alert systems.
Data Governance: Strong knowledge of data management principles, regulatory compliance, and data governance practices, particularly in the context of financial services.
Leadership & Mentorship: Ability to mentor and guide junior team members, sharing expertise in statistical analysis, AI/ML, and data quality best practices.
Problem-Solving: Excellent analytical skills to identify root causes of data quality issues and implement long-term solutions.
Collaboration: Strong ability to work with cross-functional teams, including data scientists, engineers, and financial experts, to enhance overall data quality.

Perks:

11 skills required for this role

cross-functional

data-analytics

game-texts

quality-control

aws

azure

neural-networks

python

sql

matlab

algorithms

Statistical Expertise: Advanced knowledge of statistical methods, including linear/non-linear modelling, hypothesis testing, and Bayesian techniques.
AI/ML Integration: Strong skills in applying AI/ML algorithms (e.g., neural networks, random forest, anomaly detection) for data quality checks and predictive analysis. Experience with cloud-based environments (AWS, Azure, etc.).
Quantitative Finance: Deep understanding of financial instruments, market data, and the use of quantitative methods in portfolio management and risk analysis.
Programming Skills: Proficiency in statistical programming languages (Python, R, SQL) and experience with tools like MATLAB, SAS, or similar platforms.
Automation: Experience in developing and implementing automated data validation processes, including real-time monitoring and alert systems.
Data Governance: Strong knowledge of data management principles, regulatory compliance, and data governance practices, particularly in the context of financial services.
Leadership & Mentorship: Ability to mentor and guide junior team members, sharing expertise in statistical analysis, AI/ML, and data quality best practices.
Problem-Solving: Excellent analytical skills to identify root causes of data quality issues and implement long-term solutions.
Collaboration: Strong ability to work with cross-functional teams, including data scientists, engineers, and financial experts, to enhance overall data quality.