Data Assurance Engineer, Fintech

As a Data Assurance Engineer at Optasia, your primary responsibilities include automating data validation processes and performing sanity checks on large datasets. You'll collaborate with the Data Architect to ensure the integrity of pre-processed and post-processed data, analyze missing or inaccurate data elements, and automate testing procedures for risk models and credit algorithms.

To apply for the Data Assurance Engineer position at Optasia, you should possess a BSc or MSc in Computer Science or Computer Engineering. Additionally, hands-on experience in areas such as data assurance, automation of QA processes, and credit risk modeling is highly regarded. Strong analytical skills and a passion for learning machine learning models are also essential.

Strong analytical and problem-solving abilities are at the core of the Data Assurance Engineer role at Optasia. You should also be adept at using automation tools, have experience with data processing technologies, and possess programming skills in languages like Python, Java, or R. Familiarity with both SQL and NoSQL databases is a plus.

Optasia offers a flexible remote working environment that prioritizes both productivity and work-life balance. You'll be part of a multicultural team that thrives on creativity, collaboration, and continuous learning – making it an exciting space for innovation in the fintech industry.

At Optasia, we are committed to the continuous training and professional growth of our team members. As a Data Assurance Engineer, you will have access to online training platforms, workshops, and a clear career path that encourages advancement and skill enhancement. We also value sharing knowledge and experiences among colleagues.

At Optasia, Data Assurance Engineers utilize various tools to ensure data integrity and performance. Familiarity with automation tools like Jenkins, data visualization tools such as Tableau, and programming languages like Python or MATLAB will be beneficial in executing your responsibilities effectively.

Optasia offers a competitive remuneration package along with several perks for its Data Assurance Engineers. You can expect flexible remote work options, an extra day off on your birthday, a performance-based bonus scheme, comprehensive healthcare insurance, and access to wellbeing activities such as yoga classes – all promoting a healthy work-life balance.

Data assurance is crucial in fintech as it ensures the accuracy and reliability of financial data used for decision-making processes. In your answer, emphasize the need for error-free data due to compliance regulations and the impact of incorrect data on financial outcomes.

When performing a sanity check on pre-processed data, start by outlining the expected data attributes and validating them against actual values. Focus on common anomalies, such as missing or duplicate entries, and discuss tools or scripts you would use for automation to streamline the process.

In credit risk assessment, algorithms such as logistic regression, decision trees, and ensemble methods are commonly employed. Highlight your understanding of these algorithms and mention how you have applied them in previous roles, particularly in validating their accuracy and reliability.

When discussing your experience with automation in QA processes, focus on specific tools and frameworks you've used. Provide examples of how automation improved efficiency and reduced human error in your previous roles, and explain your approach to developing and implementing automated testing solutions.

Effective documentation and tracking of production problems is essential for continuous improvement. You should discuss employing issue-tracking systems to log problems, your process for categorizing issues, and how you ensure transparent communication with relevant stakeholders for swift resolution.

In your response, mention specific tools such as Python, R, or SQL databases, explaining your preference based on their capabilities for data manipulation, analysis, and model building. Reflect on your practical experiences with these tools and how they have helped in achieving project goals.

When addressing discrepancies in data reported by clients, outline your method for conducting a thorough investigation to identify the root cause. Emphasize the importance of clear communication with clients to understand their concerns, and detail your approach to developing a resolution plan.

Answer this question by sharing a specific project experience where you applied machine learning techniques. Describe your role, the problem you aimed to solve, the methodology you followed, and the results achieved, showcasing your analytical skills and understanding of the machine learning lifecycle.

Reflect on a specific challenge you've encountered in data validation, whether it was related to data quality, integration, or scalability. Explain the steps you took to overcome it, such as enhancing validation protocols or implementing new tools, and how that led to improved data integrity.

When discussing your familiarity with big data processing tools, mention specific technologies such as Hadoop or Spark, and detail your practical experience with them. Highlight scenarios where you've used these tools to analyze large datasets, emphasizing how it added value in terms of speed, efficiency, or insights.