Data Engineer
Job description
Original text imported from Reed
Key skills
AI-extracted from the job advert
Application advice
5 AI-generated recommendations to maximise your chances.
⭐ Highlight your AWS and Apache Spark expertise at the top as this role focuses on cloud-based data infrastructure
📊 Quantify your data engineering impact: 'Built ETL pipelines processing 2TB daily, reducing latency by 45%'
🌐 Emphasise real-time analytics experience as the company needs large-scale ML and real-time processing
🎯 Showcase SaaS or digital advertising domain knowledge to demonstrate industry alignment
🤝 Detail your experience with machine learning infrastructure and data pipeline scalability
Suggested CV bullets
3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.
Add these 3 bullets under your most recent experience:
- •Architected AWS-based ETL pipelines processing 3TB daily advertising data using Apache Spark, reducing processing time from 8 hours to 45 minutes
- •Built real-time analytics infrastructure with Kafka and Snowflake, enabling ML model predictions with sub-200ms latency for 2M+ daily requests
- •Led data pipeline migration to Kubernetes, improving system reliability by 99.8% and reducing infrastructure costs by £180k annually
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Letter preview — tailored to Harnham - Data & Analytics Recruitment
Dear Hiring Manager,
Your Senior Data Engineer position at this growing SaaS scale-up immediately caught my attention, particularly the focus on machine learning infrastructure and real-time analytics within the digital advertising space. My expertise in AWS, Apache Spark, and ETL pipeline development aligns perfectly with your need to process large-scale datasets that directly impact customer outcomes.
My background in building scalable data infrastructure for high-growth technology companies has equipped me with the skills to architect robust pipelines and optimise performance at enterprise scale. I have successfully implemented real-time analytics systems and collaborated closely with data science teams to enable ML model deployment in production environments.
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Interview questions
10 questions generated from this advert.
Technical
- ›How would you design a real-time data pipeline using Kafka and Spark for processing advertising data?
- ›Explain your approach to optimising ETL performance when dealing with terabyte-scale datasets
- ›How do you ensure data quality and consistency in a distributed data architecture?
- ›Describe your experience with AWS data services and infrastructure as code using Terraform
- ›What strategies would you use to handle schema evolution in a high-volume streaming environment?
Behavioural
- ›Tell me about a time when you had to troubleshoot a critical data pipeline failure under pressure
- ›Describe a situation where you had to collaborate with data scientists to implement ML infrastructure
- ›Give an example of how you've improved data processing efficiency in a previous role
- ›Tell me about a time when you had to learn a new technology quickly to meet project deadlines
- ›Describe how you've handled conflicting priorities between different stakeholders in a data project
STAR answer examples
Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.
Tell me about a time when you had to troubleshoot a critical data pipeline failure under pressure
Describe a situation where you had to collaborate with data scientists to implement ML infrastructure