At a Glance
- Tasks: Join our team to build and optimise data pipelines on AWS for impactful analytics.
- Company: Dynamic tech company focused on data and AI innovation.
- Benefits: Flexible work options, tailored benefits, and continuous growth opportunities.
- Other info: Inclusive workplace committed to diversity and supporting employees with disabilities.
- Why this job: Make a real difference in data-driven decision making with cutting-edge cloud technologies.
- Qualifications: Experience in data engineering, strong interest in AWS, and teamwork skills.
We are seeking a detail-oriented and capable Data Engineer - AWS to join our Data & AI practice. The successful candidate will bring solid experience in data engineering, ETL/ELT pipeline development, and cloud-native platforms, with a strong focus on AWS-based data ecosystems. This role is key to supporting the design, development, and optimisation of scalable data pipelines and data platforms, enabling data-driven decision making, analytics, and downstream applications. You will contribute to ensuring data quality, reliability, and performance, through structured engineering and testing practices. You will work closely with architects, engineers, and analysts to deliver secure, high-performing data solutions, using technologies such as AWS Glue, Python/PySpark, SQL, and configuration-driven frameworks (e.g., YAML).
You should be comfortable working in a collaborative, delivery-focused environment and have a strong interest in cloud technologies, modern data platforms, and software engineering best practices.
What you’ll be doing:
- Support delivery across data engineering and platform development initiatives
- Collaborate with architects, engineers, and stakeholders to implement data solutions on AWS
- Assist in planning and executing engineering tasks, releases, and deliverables
- Build and maintain data pipelines and workflows on AWS platforms
- Support ingestion, transformation, and processing of structured and semi-structured data
- Contribute to the development of scalable, reusable data components and services
- Test and validate data pipelines and processing jobs running on AWS services
- Develop and execute data validation and reconciliation queries using SQL
- Work with AWS services including: AWS Glue, S3-based data lakes, and related data processing and orchestration services
- Support implementation of modern data platforms, including data lakes and lakehouse-style architectures
- Optimise data jobs for performance, scalability, and cost efficiency
What experience you’ll bring:
- Experience in data engineering or software engineering with a data focus
- Strong interest in cloud-based data platforms and distributed processing
- Good analytical and problem-solving skills
- Attention to detail and commitment to data quality and reliability
- Effective communication and teamwork skills
- Willingness to learn and develop in AWS and modern data engineering practices
Technical Expertise:
- Hands-on experience with AWS cloud services, especially AWS Glue
- Python / PySpark
- SQL querying and data manipulation
- Exposure to YAML or configuration-driven pipelines (desirable)
- Experience building or supporting ETL/ELT processes
- Familiarity with data lakes and/or Lakehouse concepts
- Distributed processing frameworks (e.g., Spark)
- Basic understanding of version control (e.g., Git) and CI/CD pipelines (desirable)
What we’ll offer you:
- Range of tailored benefits that support your physical, emotional, and financial wellbeing.
- Continuous growth and development opportunities through the Learning and Development team.
- Flexible work options.
We are an equal opportunities employer. We believe in the fair treatment of all our employees and commit to promoting equity and diversity in our employment practices. We are also a proud Disability Confident Committed Employer - we are committed to creating a diverse and inclusive workforce. We actively collaborate with individuals who have disabilities and long-term health conditions which have an effect on their ability to do normal daily activities, ensuring that barriers are eliminated when it comes to employment opportunities. In line with our commitment, we guarantee an interview to applicants who declare to us, during the application process, that they have a disability and meet the minimum requirements for the role. If you require any reasonable adjustments during the recruitment process, please let us know.
AWS Data Engineer employer: Women in Data®
Join a forward-thinking company that prioritises employee wellbeing and professional growth, offering tailored benefits and flexible work options. As an AWS Data Engineer, you'll thrive in a collaborative environment that champions diversity and inclusivity, while working on cutting-edge data solutions that drive impactful decision-making. With continuous learning opportunities and a commitment to equity, this role is perfect for those looking to advance their careers in a supportive and innovative setting.
StudySmarter Expert Advice🤫
We think this is how you could land AWS Data Engineer
✨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 Women in Data®!
✨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 AWS Data Engineer at Women in Data®.
✨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 Women in Data®.
✨Apply Directly through Our Website
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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 Women in Data®, 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 Women in Data®. 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 Women in Data®
✨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 Women in Data®!
✨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.
