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For Companies At a Glance
- Tasks: Lead a team of data engineers to shape analytics architecture and best practices.
- Company: Join a fast-scaling FinTech trusted by top insurers and global clients.
- Benefits: Enjoy competitive salary, remote work options, and a collaborative culture.
- Why this job: Make a real impact in a dynamic environment that values innovation and autonomy.
- Qualifications: 5+ years in data engineering with leadership experience; strong skills in Databricks and Python.
- Other info: Regular travel to London office required; bonus points for fintech experience.
The predicted salary is between 43200 - 72000 £ per year.
A fast-scaling, well-funded FinTech in the insurance and payments space is looking for a Customer Platform Data & Analytics Lead to head up a growing team of talented data engineers. This is a unique opportunity to lead the embedded analytics function within a SaaS product environment, shaping architecture and best practices while remaining hands-on with complex engineering tasks.
You'll join a high-performing, collaborative team at a company backed by major funding and trusted by leading insurers and global clients. The culture is open, inclusive and ambitious — and you’ll have real influence as they continue to scale.
What You’ll Do
- Lead, mentor and manage a squad of 5 data & analytics engineers.
- Define technical standards and drive excellence in engineering practices.
- Architect and oversee the development of cloud-native data infrastructure and pipelines using Databricks, Python, PySpark, and Delta Lake.
- Guide the implementation of embedded analytics, headless APIs, and real-time dashboards for customer-facing platforms.
- Partner with Product Owners and Tech Leads to deliver end-to-end data solutions that enhance customer experiences.
- Collaborate cross-functionally to identify reusable assets, drive data innovation and contribute to the wider data strategy.
What You’ll Bring
- 5+ years in data/analytics engineering, including 2+ years in a leadership or mentoring role.
- Strong hands-on expertise in Databricks, Spark, Python, PySpark, and Delta Live Tables.
- Experience designing and delivering scalable data pipelines and streaming data processing (e.g., Kafka, AWS Kinesis, or Azure Stream Analytics).
- Background in cloud environments such as AWS, Azure, or GCP.
- Familiarity with DevOps tools: Terraform, GitHub, CI/CD pipelines.
- Exposure to embedded analytics or building data products within SaaS platforms.
- Excellent communication, collaboration, and coaching skills.
Bonus Points For
- Experience in insurance, payments, fintech, or other regulated industries.
- Building pipelines for AI/ML models using tools like Mosaic ML.
- Working with semantic layers and advanced data modelling techniques.
- Implementing automated testing frameworks like Great Expectations or PyTest.
Ready to lead the charge in a company that values innovation, autonomy and real impact? Let’s chat. Apply now or reach out directly to munveerj@montash.com for a confidential conversation.
Customer Data Analytics Lead employer: Montash
Contact Detail:
Montash Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Customer Data Analytics Lead
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Databricks, Python, and PySpark. Having hands-on experience or projects that showcase your skills in these areas will make you stand out during discussions.
✨Tip Number 2
Prepare to discuss your leadership style and experiences in mentoring others. Since this role involves managing a team, being able to articulate how you've successfully led teams in the past will be crucial.
✨Tip Number 3
Research the company’s culture and values, especially their focus on innovation and collaboration. Be ready to share examples of how you've contributed to a positive team environment or driven innovation in previous roles.
✨Tip Number 4
Network with professionals in the FinTech space, particularly those with experience in insurance and payments. Engaging with industry peers can provide insights and potentially valuable connections that may help you during the application process.
We think you need these skills to ace Customer Data Analytics Lead
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data analytics and engineering, particularly your leadership roles. Emphasise your hands-on expertise with Databricks, Python, and cloud environments, as these are crucial for the role.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data analytics and your ability to lead a team. Mention specific projects where you've successfully implemented data solutions or improved processes, and how you can contribute to the company's goals.
Showcase Your Technical Skills: In your application, clearly outline your technical skills related to the job description. Include examples of how you've used tools like PySpark, Kafka, or AWS in previous roles, and any experience with embedded analytics or SaaS platforms.
Highlight Soft Skills: Don't forget to mention your communication and collaboration skills. Provide examples of how you've mentored others or worked cross-functionally to achieve project goals, as these qualities are essential for the Customer Data Analytics Lead position.
How to prepare for a job interview at Montash
✨Showcase Your Leadership Skills
As a Customer Data Analytics Lead, you'll be managing a team. Be prepared to discuss your leadership style and provide examples of how you've successfully mentored or led teams in the past.
✨Demonstrate Technical Expertise
Make sure you can talk confidently about your hands-on experience with Databricks, Python, and PySpark. Prepare to discuss specific projects where you've implemented these technologies, especially in building scalable data pipelines.
✨Understand the Business Context
Familiarise yourself with the FinTech industry, particularly in insurance and payments. Be ready to discuss how data analytics can enhance customer experiences in these sectors and how your role can contribute to that.
✨Prepare for Collaboration Questions
Since the role involves working cross-functionally, think of examples where you've collaborated with product owners or tech leads. Highlight your communication skills and how you've driven data innovation in previous roles.
