Data Integrity Manager

Forbes Advisor is a new initiative for consumers under the Forbes Marketplace umbrella that

provides journalist- and expert-written insights, news and reviews on all things personal finance,

health, business, and everyday life decisions. We do this by providing consumers with the knowledge and research they need to make informed decisions they can feel confident in, so they can get back to doing the things they care about most.

We are an experienced team of industry experts dedicated to helping readers make smart decisions and choose the right products with ease. Marketplace boasts decades of experience across dozens of geographies and teams, including Content, SEO, Business Intelligence, Finance, HR, Marketing, Production, Technology and Sales. We bring rich industry knowledge to Marketplaces global coverage of consumer credit, debt, health, home improvement, banking, investing, credit cards, small business, education, insurance, loans, real estate and travel.

The Data Integrity Team is a brand-new team with the purpose of ensuring all primary,

publicly accessible data collected by our researchers is correct and accurate, allowing the

insights produced from this data to be reliable. They collaborate with other teams while also

operating independently. Their responsibilities include monitoring data researched to ensure

that errors are identified and caught as soon as possible, creating detective skills for looking for

issues and mending them, setting up new configurations and ensuring they are correct, testing

new developments to guarantee data quality is not compromised, visualizing data, automating

processes, working with relevant technologies, and ensuring data governance and compliance,

playing a crucial role in enabling data-driven decision-making and meeting the organization's

data needs.

A typical day in the life of a Data Integrity Manager will involve guiding team members through

their tasks whilst looking for the next set of possible problems. They should understand about

how to automate systems, optimization techniques, and best practices in debugging, testing

and looking for issues. They work closely with other team members, offering technical

mentorship, as well as advanced Python, SQL and data visualization practices.

Responsibilities:

● Technical Mentorship and Code Quality: Mentor team members on coding standards, optimization, and debugging while conducting code and report reviews to enforce high code quality. Provide constructive feedback and enforce quality standards.

• Testing and Quality Assurance Leadership: Lead the development and implementation of rigorous testing protocols to ensure project reliability and advocate for automated test coverage.

• Process Improvement and Documentation: Establish and refine standards for version control, documentation, and task tracking to improve productivity and data quality. Continuously refine these processes to enhance team productivity, streamline workflows, and ensure data quality.

• Hands-On Technical Support: Provide expert troubleshooting support in Python, MySQL, GitKraken, Tableau and Knime, helping the team resolve complex technical issues. Provide on-demand support to team members, helping them overcome technical challenges and improve their problem-solving skills.

• High-Level Technical Mentorship: Provide mentorship in advanced technical areas, including best practices, data visualization and advanced Python programming. Guide the team in building scalable and reliable solutions to continual track and monitor data quality.

• Cross-Functional Collaboration: Partner with data scientists, product managers, and data engineers to align data requirements, testing protocols, and process improvements. Foster open communication across teams to ensure seamless integration and delivery of data solutions.

• Continuous Learning and Improvement: Stay updated with emerging data engineering methodologies and best practices, sharing relevant insights with the team. Drive a culture of continuous improvement, ensuring the teams skills and processes evolve with industry standards.

• Data Pipelines: Design, implement and maintain scalable data pipelines for efficient data transfer, transformation, and visualization in production environments.

Skills and Experience:

Minimum 8 years of relevant experience required.

• Educational Background: Bachelors or Masters degree in Computer Science, Data Engineering, or a related field. Equivalent experience in data engineering roles will also be considered.

• Data Integrity & Validation Experience: Strong ability to assess, validate, and ensure the integrity of large datasets with experience in identifying data inconsistencies, anomalies, and patterns that indicate data quality issues. Proficient in designing and implementing data validation frameworks.

• Analytical & Problem-Solving Mindset: Critical thinking with a habit of asking "why"why anomalies exist, why trends deviate, and what underlying factors are at play. Strong diagnostic skills to identify root causes of data issues and propose actionable solutions. Ability to work with ambiguous data and derive meaningful insights.

• Attention to Detail: Meticulous attention to data nuances, capable of spotting subtle discrepancies. Strong focus on data accuracy, completeness, and consistency across systems.

• Technical Proficiency: o Programming: Expert-level skills in Python, with a strong understanding of code optimization, debugging, and testing. o Object-Oriented Programming (OOP) Expertise: Strong knowledge of OOP principles in Python, with the ability to understand modular, reusable, and efficient code structures. Experience in implementing OOP best practices to enhance code organization and maintainability. o Data Management: Proficient in MySQL and database design, with experience in creating efficient data pipelines and workflows. o Tools: Advanced knowledge of Tableau. Familiarity with Knime or similar data processing tools is a plus.

• Testing and QA Expertise: Proven experience in designing and implementing testing protocols, including unit, integration, and performance testing.

• Process-Driven Mindset: Strong experience with process improvement and documentation, particularly for coding standards, task tracking, and data management protocols.

• Leadership and Mentorship: Demonstrated ability to mentor and support junior and mid-level engineers, with a focus on fostering technical growth and improving team cohesion. Experience leading code reviews and guiding team members in problem-solving and troubleshooting.

• Problem-Solving Skills: Ability to handle complex technical issues and serve as a key resource for team troubleshooting. Expertise in guiding others through debugging and technical problem-solving.

• Strong Communication Skills: Ability to clearly articulate technical challenges, propose effective solutions, and align cross-functional teams on project requirements, technical standards, and data workflows. Strong at conveying complex ideas to both technical and non-technical stakeholders, ensuring transparency and collaboration. Skilled in documenting data issues, methodologies, and technical workflows for knowledge sharing.

• Adaptability and Continuous Learning: Stay updated on data engineering trends and foster a culture of continuous learning and process evolution within the team.

• Data Pipelines: Hands-on experience in building, maintaining, and optimizing ETL/ELT pipelines, including data transfer, transformation, and visualization, for real-world applications. Strong understanding of data workflows and ability to troubleshoot pipeline issues quickly with the ability to automate repetitive data processes to improve efficiency and reliability.

Perks:

• Day off on the 3rd Friday of every month (one long weekend each month)
• Monthly Wellness Reimbursement Program to promote health well-being
• Monthly Office Commutation Reimbursement Program
• Paid paternity and maternity leave