Data Engineer in Manchester

Data Engineer in Manchester

Manchester Full-Time 45000 - 55000 £ / year (est.) No working from home possible
all

At a Glance

  • Tasks: Design and build reliable data pipelines for Channel 4's reporting and analytics.
  • Company: Join Channel 4, a leader in media and audience insights.
  • Benefits: Competitive salary, flexible working, and opportunities for professional growth.
  • Other info: Collaborative environment with a focus on innovation and best practices.
  • Why this job: Make an impact by transforming data into actionable insights for creative decision-making.
  • Qualifications: 2+ years in data engineering, strong SQL skills, and cloud platform experience.

The predicted salary is between 45000 - 55000 £ per year.

Working within the Reporting & Data Enablement team, the Data Engineer designs, builds and operates the reliable data pipelines and curated datasets that underpin Channel 4’s reporting and analytics.

DEPARTMENT DESCRIPTION

Audience Insight are trusted thought‑leaders bringing expert strategy, policy, market & audience insight – challenging and advising the business to drive delivery of Channel 4’s strategy and remit. The purpose of the new centralised Audience Insights (Subfunction) is to be independent thought leaders at the heart of Channel 4’s creative and commercial decision‑making, bringing a single source of truth and big‑picture perspective, challenging and advising the business to ensure profitable business growth through the use of data.

Within the new centralised operating Insights model (where all audience and market insight in C4 is centralised into the Audience Insight team), each C‑Suite Leader (CEO, Chief Content Officer, Chief Operating Officer, Chief Revenue Officer, Chief Marketing Officer and the Exec Committee as a body) and their Senior Leaders have a dedicated Insight Business Partner (BP) who prioritises and agrees their Insight requirements, aligned to the strategic C4 pillars. BP analysts directly source available analysis and where additional, bespoke research or specialised analysis is required, the BP will commission this from the relevant Specialist Lead, managing jointly the presentation back of recommendations to the C‑Suite Leader.

JOB PURPOSE

The role acquires, transforms and validates data from multiple internal and partner sources, ensuring quality, lineage and security so that analysts, report developers and leaders can rely on a single source of truth. The post holder embeds engineering best practice (testing, documentation, version control, CI/CD), aligns to governance standards and collaborates with Technology to deliver scalable, cost‑efficient and production‑ready data products.

KEY RESPONSIBILITIES

  • Design, build and maintain ingestion and transformation pipelines to create model‑ready, governed datasets for reporting and analysis
  • Implement data quality checks (schema validation, reconciliation, anomaly detection) and monitoring/alerting for refresh health and SLA adherence
  • Develop reusable transformation patterns and shared components (e.g., dimension/lookup handling, incremental loads, SCDs) to improve consistency and speed
  • Work with semantic‑layer and report developers to separate modelling from presentation and to uphold consistent KPI definitions
  • Follow Dev - Test - Prod workflows using deployment pipelines and version control; publish clear change logs and release notes
  • Optimise pipelines for performance, reliability and cost (partitioning, scheduling, parallelism; efficient storage and compute choices)
  • Document lineage, data dictionaries, refresh cadence, ownership and support routes; ensure discoverability via the catalogue
  • Apply data governance and security (e.g., role‑based access, PII handling, audit trails) and ensure alignment with organisational policies
  • Collaborate with Technology Data Engineering to ensure platform readiness, access, standards alignment and smooth incident/change handling
  • Contribute to enablement by sharing patterns, writing how‑to guides and supporting show‑and‑tell sessions with analysts and report developers.

KEY RELATIONSHIPS & STAKEHOLDERS

  • Reporting & Data Enablement leadership and Reporting Manager
  • Semantic‑layer developers and Report Developers
  • Technology BI and Data Engineering teams (platform, access, refresh pipelines)
  • Data Governance / Security (standards, controls, assurance)
  • Insight Business Partners and sponsored stakeholder groups.

ESSENTIAL SKILLS & EXPERIENCE

  • Proven experience in data engineering or analytics engineering (c. 2+ years) delivering reliable, governed datasets for BI and analytics
  • Strong SQL and practical experience with a cloud data platform (e.g., AWS/Azure/Fabric or equivalent) and orchestration tooling
  • Hands‑on with ELT/ETL patterns, incremental loads, performance tuning and data quality testing
  • Working knowledge of Power BI data model requirements and how pipeline design impacts report performance
  • Evidence of documentation, version control and release/change discipline
  • Clear communication and collaboration with Technology and Insight teams; ability to translate requirements into technical designs.

DESIRABLE

  • Experience with Python/Spark for transformations, familiarity with dbt or equivalent templating frameworks
  • Exposure to telemetry/monitoring (usage, refresh, cost) and automation (CI/CD)
  • Media/product/streaming industry context helpful but not essential.

Data Engineer in Manchester employer: all

Channel 4 is an exceptional employer, offering a dynamic work environment where innovation and collaboration thrive. As part of the Reporting & Data Enablement team, Data Engineers play a crucial role in shaping data-driven insights that influence strategic decisions, while enjoying opportunities for professional growth and development. With a commitment to fostering a culture of inclusivity and creativity, Channel 4 provides its employees with the tools and support needed to excel in their careers, all within a vibrant and engaging workplace located at the heart of the media industry.

all

Contact Details:

all Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Engineer in Manchester

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like all!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Engineer at all.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like all.

Apply Directly through Our Website

When you find a suitable opening like Data Engineer at all, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Data Engineer in Manchester

SQL
Problem-Solving Skills
Python
Data Governance
Automation
Data Engineering
Attention to Detail

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at all, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at all. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at all

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at all!

Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.