At a Glance
- Tasks: Lead the charge in ensuring top-notch asset data quality for impactful decision-making.
- Company: Join Riverside, a pioneering housing association transforming communities for over 90 years.
- Benefits: Enjoy competitive pay, generous holidays, flexible working, and personal development opportunities.
- Other info: Diverse and inclusive workplace with guaranteed interviews for ethnically diverse candidates.
- Why this job: Make a real difference by enhancing data quality that supports vital community services.
- Qualifications: Experience with data validation and strong analytical skills are essential.
The predicted salary is between 35237 - 43067 £ per year.
Job Description
- Salary: £38,897 (£42,992 is achieved after 12 months successful performance in the role)
- Working Hours: 35 hours per week
- Working Pattern: Monday - Friday
- The difference you will make as Asset Data Quality Lead
The Asset Data Quality Lead will lead on sourcing, assessing, monitoring, and improving the quality of asset data held within structured systems and databases.
The role focuses on ensuring asset data from internal and external sources is complete, accurate, consistent, traceable, and fit for use across asset, compliance, investment, and operational decision‑making, while ensuring opportunities to collect, improve, or enrich asset data are identified and exploited.
The role provides assurance on the quality of asset data entering core systems, reports quality issues and trends, supports reconciliation across multiple data sources, and ensures corrective actions are completed to strengthen confidence in asset information.
About You
- Experience of working with stock condition data within social housing, or a similar built asset-focused environment.
- Experience of data validation, quality assurance, cleansing, reconciliation, or exception management within structured systems or databases.
- Ability to review and interpret asset records and related source data, including property, block, component, compliance, investment, lifecycle, or attribute information.
- Ability to analyse datasets, cross-reference internal and external data sources, and identify gaps, inconsistencies, errors, and trends.
- Experience of sourcing, reviewing, or supporting the quality assurance of data received from internal teams, external providers, contractors, surveyors, or third‑party sources.
Why Riverside?
At Riverside, we’re a housing association with a difference – enhancing the everyday for all our customers.
For 90 years, we’ve been revitalising neighbourhoods and supporting communities by providing the homes they need to live full, fulfilling and rewarding lives.
We have a portfolio of over 75,000 affordable residential and retirement homes across the UK.
Our work ranges from homelessness services to social care, employment support to retirement living, and we need the best people on board to help us.
- Working With Us, You’ll Enjoy
- Competitive pay & generous pension
- 25 days holidays plus bank holidays
- Flexible working options available
- Investment in your learning, personal development and technology
- A wide range of benefits
- Diversity And Inclusion At Riverside
We are inclusive.
At Riverside, we value diversity in all its forms.
We foster a workplace where all individuals are respected, empowered, and heard.
Our commitment to inclusivity drives our success and enriches the lives of our customers and colleagues.
This role also falls under our Ethnic Diversity guaranteed interview scheme.
If you are Ethnically Diverse and demonstrate you meet the minimum criteria for the role you will be guaranteed an interview.
Role Profile
The Asset Data Quality Lead will lead on sourcing, assessing, monitoring, and improving the quality of asset data held within structured systems and databases.
The role focuses on ensuring asset data from internal and external sources is complete, accurate, consistent, traceable, and fit for use across asset, compliance, investment, and operational decision‑making, while ensuring opportunities to collect, improve, or enrich asset data are identified and exploited.
The role provides assurance on the quality of asset data entering core systems, reports quality issues and trends, supports reconciliation across multiple data sources, and ensures corrective actions are completed to strengthen confidence in asset information.
- Asset Data Quality Assurance
- Lead on assessing the quality of asset data held within asset management systems, reporting datasets, and related data sources.
- Source asset data from internal teams, external providers, contractors, surveyors, and other relevant sources, ensuring it is suitable for loading, updating, or maintaining asset management systems.
- Manage the quality of asset data entering systems by applying validation checks, resolving exceptions, and confirming that incoming data meets agreed standards before or during system update processes.
- Ensure asset data collection opportunities are identified and exploited through surveys, inspections, works programmes, compliance activity, contractor engagement, and other operational touchpoints.
- Review and validate asset records including property, block, component, compliance, investment, lifecycle, and attribute data.
- Identify missing, duplicated, poor‑quality, or inconsistent records, report findings clearly, and ensure issues are resolved.
- Assess whether asset data contains the information needed to support asset, compliance, investment, and operational decisions.
- Apply and use data standards to assess structure, traceability, and linkage to the correct asset, location, component, and source record.
- Data Review and Reconciliation
- Compare asset data from internal and external sources against source records, survey outputs, compliance records, planned works, completed works, and historical datasets to identify discrepancies.
- Review incoming asset data before it is accepted into systems, ensuring required fields, coding, evidence, source references, and relationships between records are complete and consistent.
- Investigate inconsistencies, determine likely causes, and work with relevant teams and providers to ensure quality issues are resolved.
- Stakeholder and Provider Liaison
- Provide formal feedback to internal teams, surveyors, contractors, and external providers on asset data quality issues, required improvements, and actions needed to resolve them.
- Clarify expectations for asset data capture, coding, evidence, source records, and submission standards to ensure consistency across teams and providers.
- Support onboarding and performance management of providers by reinforcing required quality standards for asset data outputs.
- Challenge recurring issues or significant failures where asset data does not meet agreed standards, elevate where required, and ensure corrective actions are completed.
- Reporting and Performance Monitoring
- Produce reports on asset data quality performance, including issues relating to completeness, accuracy, consistency, timeliness, and traceability.
- Monitor trends such as inaccurate records, missing fields, inconsistent entries, weak source evidence, and recurring data issues.
- Highlight risks and elevate where poor‑quality asset data could affect planning, compliance assurance, audit outcomes, reporting, or business decisions.
- Process Improvement
- Develop and maintain quality assurance processes, standards, and guidance for asset data creation, maintenance, and correction.
- Recommend improvements to data templates, data capture tools, validation rules, and system controls to strengthen asset data quality.
- Identify opportunities to capture additional or improved asset data through existing processes, projects, visits, inspections, surveys, and provider activity.
- Contribute to continuous improvement by identifying root causes of recurring quality issues.
- Support the development of data dictionaries, validation frameworks, naming conventions, and asset data standards.
- Success Measures
- Asset data from internal and external sources is consistently assessed, with quality issues clearly identified, reported, and resolved before or as it enters core systems.
- Internal teams and providers are challenged appropriately where data does not meet agreed standards.
- Data quality issues and trends are reported promptly, with clear escalation and timely completion of corrective actions where required.
- Data collection opportunities are proactively identified and used to improve the completeness, reliability, and value of asset data.
- Stakeholders have confidence in asset data used for investment planning, compliance reporting, operational delivery, and asset decision-making.
- Personal Specification
Essential
- Experience of working with stock condition data within social housing, or a similar built asset-focused environment.
- Experience of data validation, quality assurance, cleansing, reconciliation, or exception management within structured systems or databases.
- Ability to review and interpret asset records and related source data, including property, block, component, compliance, investment, lifecycle, or attribute information.
- Ability to analyse datasets, cross-reference internal and external data sources, and identify gaps, inconsistencies, errors, and trends.
- Experience of sourcing, reviewing, or supporting the quality assurance of data received from internal teams, external providers, contractors, surveyors, or third‑party sources.
- Ability to apply agreed data quality rules, validation checks, coding standards, and evidence requirements consistently.
- Ability to identify practical opportunities to capture, improve, or enrich asset data through existing business processes and operational activity.
- Experience of producing data quality reports, summaries, dashboards, or management information using tools such as Excel without reliance on Co‑Pilot.
- Strong Excel and Microsoft 365 skills, including the ability to analyse, manipulate, and present structured data effectively without reliance on Co‑Pilot.
- Strong attention to detail, accuracy, organisation, and prioritisation when working with data.
- Good communication skills, including the ability to raise data quality issues clearly and work with colleagues, contractors, and providers to support resolution.
- Collaborative, analytical, and quality-focused, with a proactive approach to identifying issues and supporting continuous improvement.
Desirable
- Experience working with stock condition surveyors to validate their survey results
- Experience of working with asset management systems and mobile data capture tools.
- Experience of developing, testing, or refining data quality measures for reporting dashboards or automated checks.
- Experience of developing or applying data standards, validation rules, data dictionaries, naming conventions, or assurance requirements.
- Experience of supporting audit or assurance activity relating to asset, compliance, or property data.
- Knowledge of property hierarchies, component records, lifecycle information, compliance data, evidence standards, and audit expectations.
- Ability to interpret technical property information, component condition standards, or property survey outputs.
- Awareness of data protection and information governance principles.
- Qualification in a relevant field such as data management, project management, asset management, or equivalent relevant experience.
About Us
Riverside is one of the UK’s leading not‑for‑profit social housing and regeneration organisations, owning or managing around 75,000 homes from Irvine to Kent.
We are a leading provider of supported housing services, particularly for those affected by homelessness, and our track record of transforming lives and revitalising neighbourhoods dates back over 90 years.
We have plans to build over 15,000 affordable homes over the next decade.
Our Values and Behaviours
- Creating an Inclusive Environment
- #J-18808-Ljbffr
Asset Data Quality Lead in Liverpool employer: Riverside
As a People Service Delivery Manager at our Liverpool location, you will join a dynamic and supportive work culture that prioritises employee growth and development. We offer competitive pay, generous pension schemes, and flexible working options, ensuring a healthy work-life balance while investing in your personal and professional development. Our commitment to diversity and inclusion makes us an excellent employer, providing guaranteed interview opportunities for candidates from diverse backgrounds.
StudySmarter Expert Advice🤫
We think this is how you could land Asset Data Quality Lead in Liverpool
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Riverside!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Asset Data Quality Lead at Riverside.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Riverside.
✨Apply Directly through Our Website
When you find a suitable opening like Asset Data Quality Lead at Riverside, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Asset Data Quality Lead in Liverpool
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Riverside, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Riverside. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Riverside
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Riverside!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.