Lead Data Engineer, Subscriber Solutions

Disney Cruise Line - The Walt Disney Company
London
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead data Engineer - Financial Markets - Day rate

Lead Data Engineer

Lead Data Engineer (Azure)

Lead Data Engineer

Lead AWS Data Engineer (AWS, Glue, Athena)

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

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.

Data Science Jobs in the Public Sector: Exploring Opportunities Across GDS, NHS, MOD, and More

Data science has emerged as one of the most influential fields in the 21st century, transforming how organisations make decisions, improve services, and solve complex problems. Nowhere is this impact more visible than in the UK public sector. From the Government Digital Service (GDS) to the National Health Service (NHS) and the Ministry of Defence (MOD), government departments and agencies handle vast amounts of data daily to support the well-being and security of citizens. For data enthusiasts looking to make a meaningful contribution, data science jobs in the public sector can offer rewarding roles that blend innovation, large-scale impact, and societal benefit. In this comprehensive guide, we’ll explore why data science is so pivotal to government, the roles you might find, the skills needed, salary expectations, and tips on how to succeed in a public sector data science career.

Contract vs Permanent Data Science Jobs: Which Pays Better in 2025?

Data science sits at the intersection of statistics, machine learning, and domain expertise, driving crucial business decisions in almost every sector. As UK organisations leverage AI for predictive analytics, customer insights, and automation, data scientists have become some of the most in-demand professionals in the tech job market. By 2025, data scientists with expertise in deep learning, natural language processing (NLP), and MLOps are commanding top-tier compensation packages. However, deciding whether to become a day‑rate contractor, a fixed-term contract (FTC) employee, or a permanent member of an organisation can be challenging. Each path offers a unique blend of earning potential, career progression, and work–life balance. This guide will walk you through the UK data science job market in 2025, examine the differences between these three employment models, present sample take‑home pay scenarios, and offer strategic considerations to help you determine the best fit for your career.