At a Glance
- Tasks: Build and maintain data pipelines using Apache Spark and AWS services.
- Company: Join Scrumconnect, a leading tech consultancy driving digital transformation.
- Benefits: Up to £65k salary, hybrid work, and opportunities for professional growth.
- Other info: Dynamic team culture with a focus on innovation and collaboration.
- Why this job: Make an impact by delivering trusted data assets in a modern cloud environment.
- Qualifications: Experience with Python, SQL, and data engineering best practices required.
The predicted salary is between 65000 - 65000 £ per year.
A hands-on data engineering role within a large-scale cloud data programme, responsible for building, maintaining, and troubleshooting data pipelines using Apache Spark, PySpark, Apache Airflow, and a broad suite of AWS services. You will apply strong analytical and engineering skills to deliver trusted, well-governed data assets in a modern, cloud-native environment.
This role is hybrid. Candidates must be willing and able to travel to the Newcastle office once per week. Remaining days may be worked remotely from anywhere in the UK.
You will work as a Data Engineer on a complex, cloud-based data programme - designing, building, and maintaining data pipelines that process large volumes of data across a modern AWS-native stack. Using Apache Spark and PySpark for distributed data processing, Apache Airflow for orchestration, and a range of AWS services for storage, compute, and analytics, you will help deliver reliable, well-governed data assets to downstream users.
You will apply strong data analysis skills to identify root causes of data issues, work with dimensional data models and slowly changing dimensions, and implement infrastructure as code using Terraform. Familiarity with engineering best practices and the ability to translate customer expectations into applied technical functionality are key to success in this role.
Key responsibilities
- Data pipeline development: Build and maintain scalable data pipelines using Apache Spark and PySpark, processing and transforming large datasets across distributed cloud infrastructure.
- Workflow orchestration: Configure and manage Apache Airflow DAGs for task orchestration, ensuring reliable scheduling, monitoring, and execution of data processing workflows.
- Root cause analysis: Perform data analysis to identify and resolve root causes of pipeline failures and data quality issues - including reviewing EMR output logs and CloudWatch metrics.
- Data modelling: Apply understanding of dimensional data models and slowly changing dimensions (SCD) to design and maintain well-structured, analytically trusted data assets.
- Infrastructure as code: Provision and manage cloud infrastructure using Terraform. Containerise solutions using Docker and manage deployments through GitLab CI/CD pipelines and release tagging.
- Security & encryption: Apply understanding of both Server Side and client-side encryption patterns within AWS. Work within IAM policies and data governance standards appropriate to a regulated government environment.
Technical Skills Required
- Languages & Analytics: Python - primary language for pipeline development and data processing; SQL - used for querying, transformation, and validation across data stores; PySpark - for distributed data processing using Apache Spark on AWS EMR; Familiarity with basic data structures for constructing robust, scalable solutions.
- Data processing & orchestration: Apache Spark - understanding of distributed data processing architecture and execution; Apache Airflow - configuring DAGs and managing task orchestration at scale; Jupyter Notebooks - for exploratory data analysis and pipeline prototyping; Understanding of dimensional data models and slowly changing dimensions (SCD Types 1, 2, 3); Data analysis skills to identify root cause of issues within pipelines and data assets.
- AWS services: Amazon EMR - running Spark workloads and reviewing output logs; Amazon Athena - ad hoc querying of data in S3; Amazon Textract and Comprehend - familiarity with AI/ML document extraction and NLP services; AWS S3, IAM, CloudWatch, EC2, ECR - core platform services used day-to-day; AWS console proficiency - navigating, configuring, and monitoring services; Understanding of Server Side and client-side encryption within AWS.
- Infrastructure, DevOps & delivery: Terraform - Infrastructure as Code for provisioning and managing AWS environments; Docker - containerisation of data engineering solutions; GitLab - source code management, CI/CD pipeline configuration, release tagging, and component versioning; Familiarity with engineering best practices; Ability to translate customer expectations into applied, functional technical solutions.
Technology stack at a glance: Python, PySpark, SQL, Apache Spark, Apache Airflow, Jupyter Notebooks, Dimensional modelling/SCD, AWS EMR, Amazon Athena, AWS S3, AWS IAM, AWS CloudWatch, AWS EC2/ECR, Amazon Textract, Amazon Comprehend, Terraform, Docker, GitLab CI/CD.
Data Engineer employer: Scrumconnect Consulting
Scrumconnect Consulting is an exceptional employer, offering a dynamic work culture that prioritises innovation and user-centred design. Located in the heart of the UK, we provide our Data Analysts with opportunities for professional growth through hands-on experience with cutting-edge technologies and a commitment to diversity and inclusion. Our supportive environment fosters collaboration and creativity, ensuring that every team member can contribute meaningfully to projects that impact millions of lives.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨Get Involved in Data Science Meetups
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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 Scrumconnect Consulting.
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We think you need these skills to ace Data Engineer
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!
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Craft a Tailored Cover Letter:For a full-time role at Scrumconnect Consulting, 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 Scrumconnect Consulting. 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 Scrumconnect Consulting
✨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 Scrumconnect Consulting!
✨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.
