Time since we first saw it
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Early. Similar jobs usually come down between day 5 and day 54.
From 12,144 similar postings that came down. Coming down is not always a hire.
APEX Data Engineer
Bucuresti - Ilfov, Romania
Actively hiring, first seen by us 4 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDesign and maintain PySpark pipelines on Databricks for large-scale ingestion and transformation
Design and optimize Delta Lake data models and storage structures
Build integrations between Databricks workloads and Java/Spring Boot applications
Implement data quality controls, reconciliation, and monitoring
Investigate and resolve pipeline performance, stability, and data consistency issues
From the employer’s posting
Design, develop, and maintain enterprise-grade data pipelines using PySpark on different systems as Databricks.
Design and optimize Delta Lake data models and storage structures.
Develop integrations between Databricks workloads and Java/Spring Boot applications.
Implement data quality controls, reconciliation mechanisms, and monitoring solutions.
Investigate and resolve performance, stability, and data consistency issues.
What you’ll bring
All qualificationsCore experience
- 7+ years in data or software engineering
- Proven experience designing enterprise data pipelines
- Experience integrating data platforms with Java/Spring Boot applications
- Solid knowledge of REST APIs and microservices architectures
Qualification wording
7+ years of experience in Data Engineering or Software Engineering
Proven experience designing and implementing enterprise data pipelines.
Experience integrating data platforms with Java/Spring Boot applications.
Solid knowledge of REST APIs and Microservices architectures.
Posting record
Report an errorActively hiringFirst seen by us 4 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 5, 20264 days ago
- Recorded sightings
- 5
- Last seen by us
- Nov 9, 2026
- Employer says posted
- Sep 1, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Scor is hiring
23 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Java
- Python
- SQL
- Azure
- Databricks
- Delta Lake
- Grafana
- Spark
- PySpark
Where the posting names them
We are seeking a highly skilled Senior Data Engineer to join the APEX engineering team and contribute to the design, development, and evolution of our next-generation data platform. The successful candidate will play a key role in building scalable and reliable data pipelines on Databricks, leveraging PySpark and Python to process large-scale datasets and support critical business processes. The role requires close collaboration with Backend Engineering teams to integrate data services with a Java-based ecosystem through APIs and microservices. Data Platform Development:
Backend Integration: - Develop integrations between Databricks workloads and Java/Spring Boot applications. - Design and consume REST APIs for data exchange between platform components.
- Data Governance - Experience integrating data platforms with Java/Spring Boot applications. - Solid knowledge of REST APIs and Microservices architectures.
- Data Platform: Databricks, Dataworks - Languages: Python, PySpark, SQL, Java - Backend: Spring Boot, REST APIs
Software Engineering Excellence: - Develop reusable Python frameworks and libraries to standardize data processing. - Apply software engineering best practices including clean code, testing, code reviews, and documentation.
- Apache Spark / PySpark - Python - SQL
- Python - SQL - Proven experience designing and implementing enterprise data pipelines.
- Backend: Spring Boot, REST APIs - Cloud: Microsoft Azure, Alibaba - Storage: Azure storage, Parquet
- Cloud: Microsoft Azure, Alibaba - Storage: Azure storage, Parquet - DevOps: Azure DevOps, Git
- Storage: Azure storage, Parquet - DevOps: Azure DevOps, Git - Monitoring: Grafana, ELK
Data Platform Development: - Design, develop, and maintain enterprise-grade data pipelines using PySpark on different systems as Databricks. - Build efficient ETL/ELT processes for large-scale data ingestion, transformation, and delivery.
- Participate in architecture reviews and technical design discussions. - Strong Adoption of AI tools, AI agents and Agentic AI to bring more efficiency to deliver value. Usage of AI within databricks. Proactively propose news usage / AI tools on our technical environment being open minded. Operational Excellence:
- 7+ years of experience in Data Engineering or Software Engineering. - Strong expertise in: - Databricks - Apache Spark / PySpark
Technical Environment: - Data Platform: Databricks, Dataworks - Languages: Python, PySpark, SQL, Java
- Build efficient ETL/ELT processes for large-scale data ingestion, transformation, and delivery. - Design and optimize Delta Lake data models and storage structures. - Implement data quality controls, reconciliation mechanisms, and monitoring solutions.
- Proven experience designing and implementing enterprise data pipelines. - Strong understanding of: - Delta Lake - Data Modeling
- DevOps: Azure DevOps, Git - Monitoring: Grafana, ELK
- Strong expertise in: - Databricks - Apache Spark / PySpark - Python
Your next step
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Demand for Data Engineer roles
New Data Engineer postings at the employers we follow are up 7% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
Job Summary
We are seeking a highly skilled Senior Data Engineer to join the APEX engineering team and contribute to the design, development, and evolution of our next-generation data platform.
The successful candidate will play a key role in building scalable and reliable data pipelines on Databricks, leveraging PySpark and Python to process large-scale datasets and support critical business processes. The role requires close collaboration with Backend Engineering teams to integrate data services with a Java-based ecosystem through APIs and microservices.
Data Platform Development:
- Design, develop, and maintain enterprise-grade data pipelines using PySpark on different systems as Databricks.
- Build efficient ETL/ELT processes for large-scale data ingestion, transformation, and delivery.
- Design and optimize Delta Lake data models and storage structures.
- Implement data quality controls, reconciliation mechanisms, and monitoring solutions.
- Analyze and optimize Spark workloads for performance and cost efficiency.
Backend Integration:
- Develop integrations between Databricks workloads and Java/Spring Boot applications.
- Design and consume REST APIs for data exchange between platform components.
- Support event-driven and service-oriented architectures.
- Collaborate with backend teams to ensure end-to-end data flow reliability and scalability.
Software Engineering Excellence:
- Develop reusable Python frameworks and libraries to standardize data processing.
- Apply software engineering best practices including clean code, testing, code reviews, and documentation.
- Contribute to CI/CD implementation and deployment automation.
- Participate in architecture reviews and technical design discussions.
- Strong Adoption of AI tools, AI agents and Agentic AI to bring more efficiency to deliver value. Usage of AI within databricks. Proactively propose news usage / AI tools on our technical environment being open minded.
Operational Excellence:
- Ensure reliability, observability, and supportability of production pipelines.
- Investigate and resolve performance, stability, and data consistency issues.
- Participate in production support and root cause analysis activities.
- Implement monitoring and alerting mechanisms across the data platform.
Leadership & Collaboration:
- Collaborate with architects, product owners, business analysts, and development teams.
- Contribute to technical roadmaps and platform modernization initiatives.
Technical Skills:
- 7+ years of experience in Data Engineering or Software Engineering.
- Strong expertise in:
- Databricks
- Apache Spark / PySpark
- Python
- SQL
- Proven experience designing and implementing enterprise data pipelines.
- Strong understanding of:
- Delta Lake
- Data Modeling
- Data Quality
- Metadata Management
- Data Governance
- Experience integrating data platforms with Java/Spring Boot applications.
- Solid knowledge of REST APIs and Microservices architectures.
- Experience with Git and CI/CD practices.
Soft Skills:
- Strong analytical and problem-solving abilities.
- Excellent communication and stakeholder management skills.
- Ability to lead technical discussions and influence architecture decisions.
- Self-driven, proactive, and results-oriented mindset.
- Strong collaboration skills within multicultural and distributed teams.
Technical Environment:
- Data Platform: Databricks, Dataworks
- Languages: Python, PySpark, SQL, Java
- Backend: Spring Boot, REST APIs
- Cloud: Microsoft Azure, Alibaba
- Storage: Azure storage, Parquet
- DevOps: Azure DevOps, Git
- Monitoring: Grafana, ELK
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bucuresti - Ilfov, Romania
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.