Lead Salesforce Developer

Morden
9 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer - Azure Synapse

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Salesforce Team Lead - Hybrid (1 day a week on site) - South London - £110,000

Yolk Recruitment are pleased to be working with a market leading global business who have been voted as one of the best places to work in 2024. They pride themselves on their forward-thinking culture and putting there employees first.

We are currently searching for a Salesforce Team Lead who will be a key member of a growing team. You'll be responsible for leading, mentoring, and growing a cross-functional team of talented developers, quality assurance analysts, and test automation specialists. You will play a pivotal role in designing, developing, and implementing Salesforce solutions that meet business needs, ensuring optimal use of Salesforce features and functionalities.

What you'll be doing:

Lead and manage a high-performing cross-functional development team, providing regular feedback and nurturing their technical and creative problem-solving skills.
Work closely with our Product team to prioritize, assign, and deliver upcoming work, ensuring that implemented software meets long-term business objectives.
Provide technical mentorship and guidance to junior team members.
Design, develop, test, and deploy custom Salesforce solutions, including Apex, Visualforce, Lightning Components, and integrations with other systems.
Customise Salesforce to meet specific business needs by creating custom objects, fields, workflows, and validation rules.
Integrate Salesforce with other systems using APIs and middleware tools, ensuring seamless data flow and interoperability.
Conduct code reviews and ensure the quality, performance, and security of developed solutions.
Develop software solutions to business problems, leveraging established design patterns and coding standards.
Collaborate with stakeholders to gather and analyse requirements and translate them into technical specifications.Technical skills:

7+ years as a Senior Salesforce Developer, with 3+ years leading a team.
Salesforce Application and/or Systems Architect certification(s).
Experience with Agile methodology and building a Salesforce DevOps pipeline process.
Advanced proficiency with (url removed) Platform (Apex, VisualForce, Salesforce APIs, SOQL, Unit Testing).
Proficiency with Salesforce Lightning and configuring Lightning Web components.
Experience integrating Salesforce with 3rd party tools using APIs and middleware.
Strong understanding of data migration and ETL tools.
Proficiency with code change control using BitBucket, JIRA, and Confluence.
Knowledge of sophisticated business systems integration as well as object-oriented design patterns and development.
Familiarity with Services Oriented Design Principles (SOA) and Web Services.Company Benefits

Enhanced Parental Leave
Generous annual leave
Healthcare Plan
Annual Giving Day - an extra day to give back to yourself or your community
Cycle-to-work Scheme
Pension scheme with employer contributions
Life Assurance - 3X base salary
Rewards Program - access to discounts and cashback
LinkedIn Learning License for upskilling & development

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.