At a Glance
- Tasks: Build enterprise-scale data platforms and pipelines for analytics and AI.
- Company: Join bigspark, a fast-growing tech company transforming businesses with data and AI.
- Benefits: Enjoy competitive salary, generous leave, private medical cover, and more.
- Other info: Hybrid work model with opportunities for career growth in a dynamic environment.
- Why this job: Make a real impact by harnessing data to drive business decisions.
- Qualifications: 3+ years in data engineering with strong programming skills in Python, Scala, or Java.
The predicted salary is between 50000 - 60000 £ per year.
Data Engineer – Glasgow/Edinburgh Hybrid
About bigspark
We are creating a world of opportunity for businesses by responsibly harnessing data and AI to enable positive change. We adapt to our clients' needs and then bring our engineering, development and consultancy expertise. Our people and our solutions ensure they head into the future equipped to succeed. Our clients include Tier 1 Banking and Insurance clients, and we have also been listed in the Sunday Times Top 100 Fastest Growing Private Companies.
The Role
We're looking for a Data Engineer to develop enterprise-scale data platforms and pipelines that power analytics, AI, and business decision-making. You'll work in a hybrid capacity which may require up to 2 days per week on a client premises.
What You'll Do
- Develop highly available, scalable batch and streaming pipelines (ETL/ELT) using modern orchestration frameworks.
- Integrate and process large, diverse datasets across hybrid and multi-cloud environments.
What You'll Bring
- 3+ years commercial data engineering experience
- Strong programming skills in Python, Scala, or Java, with clean coding and testing practices.
- Big Data & Analytics Platforms: Hands-on experience with Apache Spark (core, SQL, streaming), Databricks, Snowflake, Flink, Beam.
- Data Lakehouse & Storage Formats: Expert knowledge of Delta Lake, Apache Iceberg, Hudi, and file formats like Parquet, ORC, Avro.
- Streaming & Messaging: Experience with Kafka (including Schema Registry & Kafka Streams), Pulsar, AWS Kinesis, or Azure Event Hubs.
- Data Modelling & Virtualisation: Knowledge of dimensional, Data Vault, and semantic modelling; tools like Denodo or Starburst/Trino.
- Cloud Platforms: Strong AWS experience (Glue, EMR, Athena, S3, Lambda, Step Functions), plus awareness of Azure Synapse, GCP BigQuery.
- Databases: Proficient with SQL and NoSQL stores (PostgreSQL, MySQL, DynamoDB, MongoDB, Cassandra).
- Orchestration & Workflow: Experience with Autosys/CA7/Control-M, Airflow, Dagster, Prefect, or managed equivalents.
- Observability & Lineage: Familiarity with OpenLineage, Marquez, Great Expectations, Monte Carlo, or Soda for data quality.
- DevOps & CI/CD: Proficient in Git (GitHub/GitLab), Jenkins, Terraform, Docker, Kubernetes (EKS/AKS/GKE, OpenShift).
- Security & Governance: Experience with encryption, tokenisation (e.g., Protegrity), IAM policies, and GDPR compliance.
- Linux administration skills and strong infrastructure-as-code experience.
In return, you will receive:
- Competitive salary
- Generous Annual Leave
- Discretionary Annual Bonus
- Pension Scheme
- Life Assurance
- Private Medical Cover (inc family)
- Permanent Health Insurance Cover / Income Protection
- Employee Assistance Programme
- A Perkbox account
Data Engineer employer: bigspark
At bigspark, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our hybrid working model allows for flexibility while engaging with top-tier clients in the banking and insurance sectors, providing ample opportunities for professional growth and development. With competitive salaries, generous benefits, and a commitment to employee well-being, we empower our team to thrive in a supportive environment that values their contributions.
StudySmarter Expert Advice🤫
We think this is how you could land 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 bigspark!
✨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 bigspark.
✨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 bigspark.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at bigspark, 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
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 bigspark, 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 bigspark. 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 bigspark
✨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 bigspark!
✨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.
