Lead Data Engineer, Subscriber Solutions

Disney Cruise Line - The Walt Disney Company
London
11 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer (GCP)

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Disney Entertainment & ESPN Technology

On any given day at Disney Entertainment & ESPN Technology, we’re reimagining ways to create magical viewing experiences for the world’s most beloved stories while also transforming Disney’s media business for the future. Whether that’s evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to maximize flexibility and efficiency, or delivering Disney’s unmatched entertainment and sports content, every day is a moment to make a difference to partners and to hundreds of millions of people around the world.

A few reasons why we think you’d love working for Disney Entertainment & ESPN Technology

  • Building the future of Disney’s media business:DE&E Technologists are designing and building the infrastructure that will power Disney’s media, advertising, and distribution businesses for years to come.

  • Reach & Scale:The products and platforms this group builds and operates delight millions of consumers every minute of every day – from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.

  • Innovation:We develop and execute groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.

About The Role

Subscriber Data Solutions builds and maintains best in class data products enabling business teams to analyze and measure subscriber movements and support revenue generation initiatives. The Lead Data Engineer will contribute to the Company’s success by partnering with business, analytics and infrastructure teams to design and build data pipelines to facilitate measuring subscriber movements and metrics. Collaborating across disciplines, they will identify internal/external data sources, design table structure, define ETL strategy & automated Data Quality checks. You will also help mentor and guide other more junior data engineers in their data pipeline development.

Responsibilities

  • Lead the successful design and implementation of complex technical problems.

  • Lead and contribute to the design and growth of our Data Products and Data Warehouses around Subscriber movements and metrics.

  • Use sophisticated analytical thought to exercise judgement and identify innovative solutions.

  • Partner with technical and non-technical colleagues to understand data and reporting requirements, and collaborate with Data Product Managers, Data Architects and other Data Engineers to design, implement, and deliver successful data solutions.

  • Design table structures and define ETL pipelines to build performant Data solutions that are reliable and scalable in a fast growing data ecosystem.

  • Develop Data Quality checks.

  • Develop and maintain ETL routines using ETL and orchestration tools such as Airflow.

  • Serve as an advanced resource to other Data Engineers on the team, and mentor and coach more junior members of the team helping to improve their skills, knowledge, and productivity.

Basic Requirements

  • 7+ years of data engineering experience developing large data pipelines.

  • Strong understanding of data modeling principles including Dimensional modeling, data normalization principles.

  • Good understanding of SQL Engines and able to conduct advanced performance tuning.

  • Ability to think strategically, analyze and interpret market and consumer information.

  • Strong communication skills – written and verbal presentations.

  • Excellent conceptual and analytical reasoning competencies.

  • Comfortable working in a fast-paced and highly collaborative environment.

  • Familiarity with Agile Scrum principles and ceremonies.

Preferred Qualifications

  • 4+ years of work experience implementing and reporting on business key performance indicators in data warehousing environments, required.

  • 5+ years of experience using analytic SQL, working with traditional relational databases and/or distributed systems (Snowflake or Redshift), required.

  • 3+ years of experience programming languages (e.g. Python, Pyspark), preferred.

  • 3+ years of experience with data orchestration/ETL tools (Airflow, Nifi), preferred.

  • Experience with Snowflake, Databricks/EMR/Spark & Airflow a plus.

Required Education

  • Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.

  • Master’s Degree a plus.

Additional Information

#DISNEYTECH


The hiring range for this position in Santa Monica, California is $152,200 to $204,100 per year, in Seattle, Washington is $159,500 to $213,900 per year, in New York City, NY is $159,500 to $213,900 per year, and in San Francisco, California is $166,800 to $223,600 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.

#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.

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.

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.

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.