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For Companies At a Glance
- Tasks: Lead the design and implementation of innovative data solutions using cutting-edge technologies.
- Company: Join a forward-thinking organisation focused on transforming data management practices.
- Benefits: Enjoy competitive pay, flexible working options, and opportunities for professional growth.
- Other info: Dynamic role with a focus on innovation and continuous learning.
- Why this job: Make a significant impact on data architecture while collaborating with tech enthusiasts.
- Qualifications: Proven experience in data engineering and strong problem-solving skills required.
The predicted salary is between 80000 - 100000 ÂŁ per year.
Reporting to the Head of Enterprise Data, we are seeking a Lead Data Engineer with deep expertise in Data Vault design and a proven track record of delivering high‑quality data solutions. You will partner closely with our technology and development leads to define, shape, and implement Arch’s data architecture and data management practices. This is a dynamic, hands‑on role ideal for someone who is passionate about technology and motivated by the opportunity to drive meaningful, long‑term impact across the organisation’s data landscape.
Key responsibilities
- Landscape understanding & platform support: Lead the effort to establish a clear view of our current database and application estate, supporting operations in maintaining, rationalising, and optimising existing data platforms.
- Strategic platform development: Help design and build the strategic data platforms Arch is moving toward, ensuring they are robust, scalable, and aligned with business needs.
- Standards, tooling & best practices: Define and implement the tools, patterns, and practices required to deliver efficient, high‑quality, and data‑driven engineering solutions.
- Technology evangelism: Act as an advocate for modern data engineering approaches, championing innovation and continuous improvement across teams.
- Innovation & awareness: Stay current with emerging technologies, techniques, and capabilities—applying relevant advancements to improve delivery and engineering effectiveness.
- Leadership & problem solving: Demonstrate strong leadership behaviours, paired with practical operational experience and an ability to tackle complex technical challenges.
- Collaboration & roadmap shaping: Work closely with architecture and technology leaders to define the roadmap for evolving Arch’s data engineering practices, grounded in industry trends and current organisational capabilities.
Key tasks and responsibilities
- Collaborate with business stakeholders to translate requirements into actionable technical tasks and ensure their successful delivery.
- Work with ancillary teams to support the data warehouse, driving improvements in data quality, reporting, and coordination with source system teams.
- Partner closely with the data architecture team to enhance the data warehouse, contributing to design discussions, reviewing architectural plans, and ensuring alignment with best‑practice standards.
- Operate effectively within agile sprint teams, contributing to sprint planning, daily stand‑ups, and reviews while supporting continuous improvement of team processes.
- Design, build, and manage ELT processes to integrate data from multiple sources into the data warehouse, ensuring consistency and quality across systems.
- Monitor and optimise data pipelines to ensure reliable, efficient operation.
- Maintain a strong focus on data quality, demonstrating attention to detail and rigorous validation practices.
- Deliver high‑quality code end‑to‑end — including design, implementation, unit testing, refactoring, and documentation.
- Automate deployment processes to ensure consistent, repeatable, and reliable releases.
- Monitor automated systems, proactively identifying and resolving issues.
- Write, maintain, and improve unit tests to ensure code quality and early issue detection.
- Collaborate with developers to improve test coverage, reliability, and overall engineering standards.
- Ensure all new code meets established standards for readability, performance, security, and documentation, including performing and participating in code reviews.
- Apply DevSecOps principles to integrate security into all stages of the development lifecycle.
- Integrate and manage tools across the data stack — including ETL platforms, orchestration tools, and data management components — ensuring seamless interoperability and optimal performance.
- Continuously learn and experiment with modern technologies, applying new knowledge to improve systems, processes, and overall engineering maturity.
- Stay informed on industry trends, using this insight to drive innovation and optimise data engineering practices.
Experience requirements and skills
- Extensive hands‑on experience designing, developing, and maintaining data pipelines and ETL/ELT processes.
- Data Vault 2.0 certification highly desirable.
- Expert‑level experience with Snowflake or other cloud‑based data warehouse technologies.
- Strong hands‑on experience with orchestration tools such as Airflow (or equivalent).
- Deep knowledge of relational and non‑relational databases, including RDBMS proficiency and modern data warehouse design.
- Familiarity with DevOps practices, CI/CD pipelines, automation, and containerisation technologies (e.g., Docker, Harness, Kubernetes).
- Knowledge of cloud‑native architectures and modern application frameworks, including REST APIs, microservices, Spring Boot/.NET Core, GitHub, Jenkins, OpenShift, BPM, SQL, Oracle, NoSQL, AMQP/Kafka.
- Strong understanding of private cloud, IaaS, PaaS, and SaaS models, with extensive experience across Azure and AWS.
- Broad understanding of modern software engineering methods, tools, and best practices.
- Proficiency in SQL (including ANSI SQL) and experience with Python or other programming languages used in data engineering.
- Experience with application and data testing automation tools and best practices.
- Strong grounding in agile methodologies, with proven experience applying them to large‑scale technology delivery.
- Strong strategic thinking and long‑term planning capabilities, with the ability to balance ideal architectural solutions against pragmatic business needs.
- Excellent communication and interpersonal skills for collaborating with diverse technical and non‑technical stakeholders.
- Strong analytical, problem‑solving, and decision‑making skills, with a focus on delivering reliable, scalable, and high‑quality solutions.
Experience & Education
- Required knowledge and skills are typically obtained through a Bachelor’s degree (or equivalent experience) and 10+ years of relevant experience in software development, systems infrastructure, and architecture design — including project management, business analysis, and hands‑on data engineering.
- Data Vault 2.0 certification is highly preferred.
Lead Data Engineer employer: Arch Insurance
Contact Detail:
Arch Insurance Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Engineer
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the data engineering space. Attend meetups, webinars, or even just grab a coffee with someone in the industry. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Data Vault design or cloud-based technologies like Snowflake. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with ETL processes, data pipelines, and agile methodologies. Practice common interview questions and think about how you can demonstrate your leadership abilities.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Lead Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Lead Data Engineer role. Highlight your experience with Data Vault design and any relevant technologies like Snowflake or Airflow. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for data engineering and how you can drive innovation at StudySmarter. Don’t forget to mention specific projects that showcase your expertise in data architecture.
Showcase Your Problem-Solving Skills: In your application, give examples of complex technical challenges you've tackled. We love seeing how you approach problem-solving, especially in a collaborative environment. It’s all about demonstrating your leadership and analytical skills!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're keen on joining the StudySmarter team!
How to prepare for a job interview at Arch Insurance
✨Know Your Data Vault Inside Out
Make sure you brush up on Data Vault design principles before the interview. Be ready to discuss your past experiences with it, and how you've implemented it in real-world scenarios. This will show that you not only understand the theory but can also apply it effectively.
✨Showcase Your Technical Skills
Prepare to demonstrate your expertise with tools like Snowflake and orchestration platforms such as Airflow. Bring examples of projects where you've successfully built data pipelines or managed ELT processes. This hands-on experience is crucial for a Lead Data Engineer role.
✨Emphasise Collaboration
Since this role involves working closely with various teams, be ready to share examples of how you've collaborated with stakeholders in the past. Highlight your communication skills and how you've translated technical requirements into actionable tasks for non-technical team members.
✨Stay Current with Industry Trends
Demonstrate your passion for technology by discussing recent advancements in data engineering. Mention any new tools or methodologies you've explored and how they could benefit the organisation. This shows you're proactive and committed to continuous improvement.