At a Glance
- Tasks: Manage and enhance data operations for impactful research projects.
- Company: Fast-growing insights organisation with a global presence.
- Benefits: Competitive salary, hybrid work, and opportunities for professional growth.
- Other info: Dynamic role with a focus on automation and operational excellence.
- Why this job: Join a team that values innovation and continuous improvement in data operations.
- Qualifications: Experience in market research, data processing, and strong communication skills.
The predicted salary is between 49500 - 60500 £ per year.
A well-established and fast-growing insights organisation with an international presence and a strong reputation for delivering high-quality data-driven projects. Their teams combine research expertise, technology, and operational excellence to help clients make informed business decisions. With significant investment in automation and modern data infrastructure, they offer an environment where innovation and continuous improvement are actively encouraged.
You will be responsible for managing and improving operational delivery across a range of quantitative research and data programmes. Key responsibilities include:
- Delivering survey scripting, data processing, reporting operations, and workflow automation
- Designing, maintaining, and improving operational tools and automated processes
- Managing delivery priorities and resource allocation across multiple projects
- Identifying and mitigating delivery risks before they impact project outcomes
- Implementing and strengthening quality assurance and governance processes
- Acting as an escalation point for complex technical delivery challenges
- Working closely with stakeholders to communicate technical issues in a clear and commercially focused way
- Supporting the continuous improvement of operational frameworks, automation, and delivery standards
Your Skills & Experience
- Strong experience within market research, research operations, data processing, or a similar environment
- Hands-on survey scripting experience using platforms such as Decipher, Qualtrics, Forsta, Dimensions, or equivalent
- Experience managing quantitative data workflows and reporting outputs
- Strong data cleaning, transformation, and data management capabilities
- Knowledge of automation workflows, APIs, and process optimisation
- Exposure to tools such as SPSS, Q, DisplayR, or similar analytical platforms
- Excellent stakeholder management and communication skills
- Commercial awareness and the ability to balance quality, timelines, and operational efficiency
- Experience with Python, SQL, JavaScript, or other automation technologies is highly desirable
Data Operations Manager employer: Harnham
As a leading specialist data consultancy, we pride ourselves on fostering a collaborative and innovative work culture that empowers our Analytics Engineering Consultants to thrive. With a focus on professional development, we offer extensive growth opportunities and the chance to work on high-impact projects across diverse industries, all while enjoying the flexibility of remote work in the UK. Join us to be part of a team that values your expertise and encourages you to influence strategy and drive meaningful change for our clients.
StudySmarter Expert Advice🤫
We think this is how you could land Data Operations Manager
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Data Operations Manager
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 Harnham, 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 Harnham. 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 Harnham
✨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 Harnham!
✨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.
