Data Engineer Jobs in Manchester
Are you a talented data engineer seeking new opportunities in Manchester? Applause IT can connect you to top data engineering jobs in Manchester and can help you take the next step in your career.
Why choose to work in data in Manchester?
Which companies call Manchester home?
AutoTrader - a leading online automotive marketplace, uses data engineering to manage and process vast amounts of data related to car listings, user interactions, and market trends.
Key applications:
- Enhancing search algorithms to improve user experience.
- Developing predictive models for market trends and pricing strategies.
- Personalising marketing campaigns based on user data analysis.
The Co-operative Group - one of the UK's largest consumer co-operatives, utilises data to streamline operations across its retail, insurance, and funeralcare businesses.
Key Utilisations:
- Implementing
data-driven inventory management systems.
- Enhancing
customer loyalty programmes through data insights.
- Optimising
supply chain operations to reduce costs and improve efficiency.
BBC - the BBC relies heavily on data engineering to manage its vast digital content library and analyse viewer interactions. Data engineers at the BBC create scalable data solutions that process streaming data, allowing the organisation to understand audience preferences and tailor content accordingly.
Key Utilisations:
- Developing
recommendation engines for personalised content delivery.
- Analysing
audience engagement metrics to inform content strategy.
- Optimising
digital platforms for improved user experience.
Bet365 - a leading online betting company, utilises data to process and analyse large volumes of transactional data and it plays a critical role in developing systems that support real-time betting analysis, fraud detection, and customer insights.
Key Utilisations:
- Building
real-time data processing pipelines for in-play betting.
- Implementing
machine learning models for fraud detection.
- Personalising
customer offers and promotions based on behaviour analysis.
Sainsbury's Tech - the technology division of Sainsbury's, uses data engineering to support its retail operations and enhance customer experiences.
Key Utilisations:
- Optimising
logistics and supply chain through data analytics.
- Enhancing
customer shopping experiences with personalised recommendations.
- Streamlining
e-commerce operations for better efficiency and user satisfaction.
Barclays - this financial giant leverages technology to manage financial data, ensure compliance, and drive customer engagement.
Key Utilisations:
- Developing
real-time analytics platforms for risk management.
- Implementing
data-driven solutions for customer engagement and retention.
- Ensuring
compliance with financial regulations through robust data governance.
Do you need to know how to code?
Data engineering is fundamentally about coding and software
development, which involves building the infrastructure that enables
organisations to manage and analyse large volumes of data effectively. Here are
some key aspects of coding in data engineering:
- Programming Languages: Data engineers need to be proficient in programming languages such as Python, Java, Scala, and SQL. These languages are essential for developing data pipelines, automating data workflows, and performing data transformations.
- Data Pipelines: Writing code to create scalable data pipelines is a core responsibility. These pipelines are designed to extract data from various sources, process it, and load it into data warehouses or data lakes for analysis.
- Database Management: Understanding and working with databases is crucial. Data engineers write scripts and queries to manipulate data within databases, ensuring it is clean, organised, and accessible for analytical purposes.
- ETL Processes: Coding is heavily involved in ETL (Extract, Transform, Load) processes, where data engineers extract data from different sources, transform it into a usable format, and load it into storage systems.
- Automation and Optimisation: Data engineers use coding to automate repetitive tasks, optimise data processing workflows, and improve the performance and efficiency of data systems.
Looking for your next Data Engineer job with Applause IT
What are the main responsibilities of a Data Engineer?
Data engineers are crucial players in the management and utilisation of data within an organisation. Their responsibilities span a variety of technical and strategic tasks, ensuring that data is accessible, reliable, and usable for decision-making processes.
Design and Implement Data Pipelines
- Pipeline
Architecture: Responsible for designing the
architecture of data pipelines, ensuring they are scalable and efficient.
This involves selecting appropriate tools and frameworks to handle data
extraction, transformation, and loading (ETL).
- Data
Integration: Integrating data from multiple sources, such as
databases, APIs, and third-party applications, into a centralised system.
This requires understanding various data formats and communication
protocols.
- Automation:
Automating data workflows is a crucial responsibility, allowing data to be
processed and delivered in real-time or on a scheduled basis. This reduces
manual intervention and enhances data accuracy.
Data Warehousing and Storage Management
- Database
Design: Data engineers design and optimise databases and data
warehouses to store large volumes of structured and unstructured data
efficiently. They ensure the databases are well-organised and support fast
query performance.
- Data
Lakes: They may also manage data lakes, which store raw data in its
native format. Data engineers ensure these systems are secure, scalable,
and capable of supporting complex analytical queries.
- Data
Governance: Implementing data governance policies to ensure data
quality, security, and compliance with regulatory requirements is a
critical responsibility. This involves setting up access controls and
auditing mechanisms.
Data Modelling and Transformation
- Data
Modelling: Data engineers create data models that define the
relationships between different data entities, supporting analytical and
reporting needs. This involves using techniques like star schema or
snowflake schema for relational databases.
- Data Transformation: Transforming raw data into a usable format is essential for analysis. Data engineers apply various transformation techniques, such as aggregating, filtering, and enriching data to meet business requirements.
Performance Optimization
- Query Optimization: Ensuring fast and efficient data retrieval by optimising SQL queries and database performance is a key responsibility. This involves indexing, partitioning, and tuning database settings.
- Resource Management: Data engineers manage computational resources effectively, ensuring that data processing tasks are executed within budget and performance constraints. This includes monitoring and adjusting resource allocation on cloud platforms.
Collaboration and Cross-Functional Work
- Team Collaboration: Working closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions that align with business objectives. This collaboration ensures that data solutions meet the organisation's strategic goals.
- Stakeholder Communication: Effective communication with stakeholders to gather requirements, provide updates, and discuss data insights. Data engineers need to translate technical concepts into understandable terms for non-technical audiences.
Innovation and Tool Evaluation
- Technology Exploration: Staying updated with the latest data engineering tools and technologies, and evaluating their potential to improve existing data infrastructure. This includes assessing new databases, processing frameworks, and analytics tools.
- Process Improvement: Continuously seeking opportunities to enhance data engineering processes, whether through automation, new methodologies, or adopting cutting-edge technologies to improve efficiency and scalability.
Monitoring and Troubleshooting
- System Monitoring: Regularly monitoring data systems to ensure they are running smoothly and efficiently. This includes setting up alerts and dashboards to track performance metrics and system health.
- Troubleshooting: Quickly identifying and resolving issues related to data quality, pipeline failures, and system outages. This involves analysing error logs, conducting root cause analysis, and implementing fixes to prevent future occurrences.
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