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- Tasks: Lead the transformation of healthcare data into actionable insights using cloud-native pipelines.
- Company: Join a forward-thinking company dedicated to improving patient care through data engineering.
- Benefits: Enjoy hybrid or remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career advancement.
- Why this job: Make a real impact in healthcare by optimising data flows that enhance patient outcomes.
- Qualifications: 3+ years in data engineering, expert SQL skills, and proficiency in Python or Scala.
The predicted salary is between 60000 - 80000 £ per year.
Lead the modernization of our data infrastructure as a Data Engineer for nimble. You'll architect scalable cloud-native pipelines using Microsoft Fabric and Databricks to transform healthcare data—claims, EMR/EHR, HL7/FHIR—into actionable insights that drive revenue cycle optimization and clinical outcomes.
Why This Role Matters
Healthcare data engineering is mission-critical: clean, governed data flows directly impact financial accuracy, compliance, and the decisions that improve patient care. Your ETL/ELT pipelines enable our analytics and data science teams to unlock the full potential of healthcare data.
Key Responsibilities
- Design, build, and optimize ETL/ELT pipelines using Azure Synapse, Databricks, and Snowflake
- Develop robust data models and schemas for healthcare datasets, including claims, EMR/EHR, HL7, and FHIR standards
- Write and optimize SQL queries for performance across large healthcare datasets
- Implement data governance, quality frameworks, and HIPAA compliance controls
- Collaborate with analytics, data science, and business teams to define data requirements
- Monitor and troubleshoot data pipeline health and performance
- Develop Python or Scala code for complex transformations and data processing
- Support Power BI and analytics teams with data modeling and performance optimization
- Document data lineage, transformations, and technical architecture
Requirements
- 3+ years of professional data engineering or ETL/ELT development experience
- Expert-level SQL skills with proven optimization experience
- Proficiency in Python, Scala, or similar data processing languages
- Hands‑on experience with cloud data platforms (Azure Synapse, Snowflake, Databricks, or equivalent)
- Understanding of healthcare data standards (HL7, FHIR, claims data structures)
- Strong grasp of data modeling, normalization, and schema design
- Experience with data versioning, CI/CD pipelines, and data quality frameworks
Preferred Qualifications
- Experience with Microsoft Fabric or Azure Data Factory
- Knowledge of HIPAA compliance and healthcare data security
- Background in healthcare, RCM, or claims processing
- Experience with dbt (data build tool) or equivalent transformation frameworks
- Exposure to dimensional modeling and data warehousing best practices
What Success Looks Like
- In 90 days: Deploy first cloud pipeline to production; complete HIPAA training; establish data quality baseline metrics
- In 6 months: Reduce data pipeline latency by 30%; expand healthcare data models to include new sources; build reusable transformation components
- Ongoing: Maintain 99.5%+ pipeline uptime; mentor junior engineers; drive architectural improvements for scale and performance
Data Engineer employer: Nimble Solutions
Contact Detail:
Nimble Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even local events. The more people you know, the better your chances of landing that Data Engineer role.
✨Show Off Your Skills
Create a portfolio showcasing your projects, especially those involving ETL/ELT pipelines or cloud data platforms. Share it on LinkedIn or GitHub to grab the attention of hiring managers.
✨Ace the Interview
Prepare for technical interviews by brushing up on SQL queries and data modelling. Practice explaining your thought process clearly, as communication is key in collaborative environments like ours.
✨Apply Through Our Website
Don’t forget to apply directly through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Engineer role. Highlight your experience with ETL/ELT pipelines, SQL skills, and any cloud platforms you've worked with. We want to see how your background aligns with our needs!
Showcase Your Projects: Include specific projects where you've designed or optimised data pipelines. If you've worked with healthcare data, mention it! This helps us understand your hands-on experience and how you can contribute to our mission.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Explain why you're passionate about data engineering in healthcare and how you can help us drive revenue cycle optimisation. Keep it engaging and personal—let us know who you are!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Nimble Solutions
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Azure Synapse, Databricks, and SQL. Brush up on your Python or Scala skills too, as you'll likely be asked to demonstrate your coding abilities during the interview.
✨Understand Healthcare Data Standards
Familiarise yourself with healthcare data standards such as HL7 and FHIR. Being able to discuss how these standards impact data engineering will show that you understand the industry and can contribute effectively from day one.
✨Prepare for Scenario-Based Questions
Expect questions that ask you to solve real-world problems related to data pipelines and ETL processes. Think through examples from your past experience where you optimised a pipeline or improved data quality, and be ready to explain your thought process.
✨Show Your Collaborative Spirit
This role involves working closely with analytics and data science teams. Be prepared to discuss how you've collaborated in the past, and highlight any experiences where you’ve defined data requirements or mentored others. This will demonstrate your ability to work well in a team environment.