Data Engineering Manager

Meta
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineering Manager

BDO Digital Data Architecture and Engineering Senior Manager/Director

Data Science Manager – Property Tech – London

BI and Data Engineering Lead

BI and Data Engineering Lead

BI and Data Engineering Lead

Meta is seeking a leader in our Data Engineering team to work closely with Product Managers, Data Scientists and Software Engineers to support product launches and roadmaps by building the data architecture that informs and drives insight. In this role, there will be a direct link between your work, company growth, and user satisfaction. You'll work with some of the brightest minds in the industry, with one of the richest data sets in the world, using cutting‑edge technology, and see your efforts affect products and people on a regular basis.


Data Engineering Manager Responsibilities

  • Drive the mission and strategy for Business Intelligence and Data Warehousing across a product vertical
  • Build and lead a high‑quality BI and Data Warehousing team, designing it to scale
  • Develop cross‑functional relationships with stakeholders to understand data needs and deliver on those needs
  • Manage data warehouse plans, drive data quality, and ensure operational efficiency
  • Design, build, and launch new data models and pipelines in production
  • Deliver high‑impact dashboards and data visualizations
  • Define and manage Service Level Agreements for all data sets and processes running in production
  • Demonstrated operational skills to drive efficiency and speed

Minimum Qualifications

  • 8+ years of experience in Business Intelligence and Data Warehousing
  • Experience scaling and managing teams with 3 or more individuals
  • Communication and leadership experience, with experience initiating and driving projects
  • Project management experience
  • Data infrastructure and architecture experience
  • Experience in SQL or similar languages
  • Development experience in at least one object‑oriented language (Python, Java, etc.)
  • BA/BS in Computer Science, Math, Physics, or other technical fields

Preferred Qualifications

  • Experience in large scale, warehouse‑wide privacy / risk projects
  • Experience in influencing, collaborating, and driving execution with stakeholders across pillars and multiple product groups

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.


Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.


#J-18808-Ljbffr

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.

Careers

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

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.