Data Engineering Lead

Data Engineering Lead

City of London Full-Time 43200 - 72000 Β£ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Lead a dynamic team to build innovative data platforms and enhance operational efficiency.
  • Company: Join Elsevier, a global leader in information and analytics for science and healthcare.
  • Benefits: Enjoy generous holidays, private medical benefits, and flexible working arrangements.
  • Why this job: Make a real impact on healthcare and research while developing your leadership skills.
  • Qualifications: Experience in team leadership, SDLC best practices, and modern data technologies.
  • Other info: Embrace a diverse and inclusive culture with excellent career growth opportunities.

The predicted salary is between 43200 - 72000 Β£ per year.

Overview

Data Engineering Lead

Do you enjoy Team Management? Are you a team player?

About the Company

A global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world’s grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world.

The Team

The Enterprise Data Platforms and Services (EDPS) team is a central technology group responsible for building, administering, governing, and setting global standards for a growing number of Elsevier strategic data platforms and services. The capabilities we are responsible for enable data to be collected, accessed, processed and integrated across a wide range of digital business solutions, used by functions including billing and order management, customer and product master data management, and business analytics and insights delivery. The technology underpinning these capabilities includes Snowflake, Tableau, DBT, Talend, Collibra, Kafka/Confluent, Astronomer/Airflow, and Kubernetes. This forms part of a longer-term strategic direction to implement Data Mesh, and with it establish shared platforms that enable a connected collection of enterprise-ready time-saving services, applying a self-service first approach. Our mission is to enable frictionless experiences for all Elsevier colleagues, so that they can openly and securely consume and produce trustworthy data, enhancing everyday colleague and customer interactions and decisions.

The Role

As the Software Engineering Lead, you will nurture a high performing cross-functional squad of software and data engineers. This squad is responsible for a growing number of strategic capabilities and components that serve a large number of engineering, data science, and analytics use cases and stakeholders. You will be the technical subject matter expert overseeing the squad building an Enterprise Data Platform that supports both operational and analytical use cases.

You will combine technical expertise with strong stakeholder engagement to understand business needs when designing a fit-for-purpose technical solution. You will map user requirements and diverse interactions with platform components to inform implementation decisions, and collaborate closely with other technology teams to promote a culture of contributing towards shared services.

Your success will be measured by increases in the number of teams adopting and contributing to platform capabilities and shared repositories, and clear improvements in technical efficiency and value gains.

Key Responsibilities and Accountabilities

  • Accountable for team performance – manage a high performing agile delivery squad, nurturing skills, trust, and relationships through coaching and mentoring.

  • Accountable for releases – set technical development and coding standards that comprise a robust SDLC, and review releases to ensure standards are met.

  • Accountable for shared services – build common frameworks and patterns that can be reused, contributed to, and reliably deployed by other teams via self-service.

  • Accountable for best practices – establish component-specific guidelines in collaboration with the team, wider engineering teams, architecture, end-users, data product owners, and enablement teams, and promote them through regular knowledge sharing sessions.

  • Accountable for operational efficiency – drive improvements in efficiency, reliability, and scalability supported by logging, monitoring and observability as foundational capability.

  • Responsible for adoption – promote platform capabilities through technical communities of practice leadership, maintain high internal standards for documented processes and guides, and capture and act on user feedback.

  • Responsible for platform evolution – collaborate with stakeholders to identify capability gaps and drive discussions required to make a case for change.

  • Responsible for technical governance – establish and manage the technical design authority process for each capability to govern self-service use of the platform.

  • Essential Skills & Experience:

  • Team leadership – driven line manager and technical lead, focused on coaching and mentoring to motivate cross-functional squads.

  • SDLC – applied understanding of SDLC best practices, with a track record of improving SDLC and DataOps/DevOps maturity.

  • Agile delivery – facilitating ceremonies, removing impediments, refining requirements, and fostering iterative improvement.

  • Modern data stack – hands-on deployment and governance of enterprise technologies at scale (e.g., Snowflake, Tableau, DBT, Fivetran, Airflow, AWS, GitHub, Terraform) for self-service workloads.

  • Coding languages – Python, JavaScript, and Jinja templating for ETL/ELT data applications, data pipelines, and stored procedures.

  • Thought leadership and influencing – strong interest in the data platforms landscape with proposals supported by research and value delivery.

  • Solution design and architecture – ability to create comprehensive technical design documents, including architecture and infrastructure artifacts to support scalable, secure, efficient data platforms with reliable data flow from ingestion to consumption.

  • AWS cloud ecosystem – deep knowledge of AWS data and analytics services and production-grade data solutions.

  • Prioritisation – adaptable to changing needs with professional, flexible, and pragmatic responses to evolving priorities while mitigating impacts.

  • Data and technology governance – applying data management, privacy and security practices at scale to ensure compliant platform use.

  • Work in a way that works for you

Work in a way that works for you

We promote a healthy work/life balance across the organisation. With an average length of service of 9 years, we offer an appealing working prospect. We have wellbeing initiatives, shared parental leave, study assistance and sabbaticals to help you meet responsibilities and long-term goals.

Working remotely from home or in our office in a flexible hybrid style. Working flexible hours to fit your productivity peaks.

Working with us

We are an equal opportunity employer with a commitment to help you succeed. We promote a diverse, inclusive, collaborative, and innovative environment where everyone has a part to play.

Working for you

At Elsevier, your wellbeing and happiness are key to a long and successful career. Benefits include: generous holiday allowance, learning time, private medical benefits, wellbeing programs, life assurance, pension, long service awards, share option schemes, travel loan, parental leave, emergency care support, and RELX Cares days. We also offer employee discounts and access to various learning resources.

About the Business

A global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. We are committed to equal opportunity and provide an accessible hiring process. If you require accommodation, please let us know through provided contacts.

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Data Engineering Lead employer: RELX INC

At Elsevier, we pride ourselves on being an exceptional employer that champions a healthy work/life balance and fosters a culture of inclusivity and collaboration. Our commitment to employee wellbeing is reflected in our generous benefits package, which includes private medical coverage, flexible working arrangements, and ample opportunities for professional growth through learning initiatives. Join us in our mission to advance science and improve health outcomes while enjoying a supportive environment that values your contributions and promotes a sustainable future.
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Contact Detail:

RELX INC Recruiting Team

StudySmarter Expert Advice 🀫

We think this is how you could land Data Engineering Lead

✨Tip Number 1

Network like a pro! Reach out to your connections on LinkedIn or attend industry meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.

✨Tip Number 2

Prepare for interviews by researching the company and its culture. Understand their mission and values, especially how they contribute to healthcare and science. This will help you tailor your answers and show you're genuinely interested.

✨Tip Number 3

Practice makes perfect! Conduct mock interviews with friends or use online platforms. Focus on articulating your experience with data engineering tools like Snowflake and Tableau, as well as your leadership skills.

✨Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!

We think you need these skills to ace Data Engineering Lead

Team Leadership
Agile Delivery
SDLC Best Practices
Modern Data Stack
Python
JavaScript
Jinja Templating
Solution Design and Architecture
AWS Cloud Ecosystem
Data and Technology Governance
Thought Leadership
Prioritisation
Coaching and Mentoring
Stakeholder Engagement
Technical Governance

Some tips for your application 🫑

Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Data Engineering Lead role. Highlight your team management experience and any relevant technical expertise to catch our eye!

Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about data engineering and how you can contribute to our mission. Share specific examples of your past successes in team leadership and project delivery.

Showcase Your Technical Skills: Don’t forget to mention your hands-on experience with technologies like Snowflake, Tableau, and Python. We love seeing how you've applied these tools in real-world scenarios, so be specific!

Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!

How to prepare for a job interview at RELX INC

✨Know Your Tech Stack

Familiarise yourself with the technologies mentioned in the job description, like Snowflake, Tableau, and Python. Be ready to discuss your hands-on experience with these tools and how you've used them to solve real-world problems.

✨Showcase Your Leadership Skills

As a Data Engineering Lead, you'll need to demonstrate your ability to manage and mentor a team. Prepare examples of how you've successfully led teams in the past, focusing on your coaching style and how you foster collaboration.

✨Understand Agile Methodologies

Brush up on Agile principles and be prepared to discuss how you've implemented Agile practices in previous roles. Highlight specific ceremonies you've facilitated and how you've removed impediments to ensure smooth delivery.

✨Prepare for Scenario-Based Questions

Expect questions that assess your problem-solving skills and technical governance. Think of scenarios where you've had to make tough decisions regarding data management or platform evolution, and be ready to explain your thought process.

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