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

Lead Data Engineer

Lead Data Engineer - Snowflake, DBT, Airflow - London - £100k

Lead Data Engineer - Manchester - Hybrid - £75k - £80k

Lead data Engineer - Financial Markets - Day rate

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.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.