Description review
Senior Data Engineer (Scala)
Empat · Remote · back to the listing
HR standards
59/100
needs work
Title ↔ description
93/100
strong
Reads as
Data Engineer
100% confident
How others title the same work
Large employers
- Data Engineer, Product Analytics Meta
- Senior Staff Data Engineer Mozilla
- Staff, Analytics Engineer, GTM Data Science & Analytics Twilio
- Senior Software Engineer - Data Platform Coinbase
- Senior Security Data Engineer Doordash
Startups
- Business Intelligence Engineer GoSats
- IPinfo.io | Data Engineer | REMOTE (Anywhere) | Full-time IPinfo.io
- Senior Data Engineer Camber
- Senior Data Engineer Instrumentl
- Cardog | Toronto, Canada or REMOTE | Full-time Cardog
What the listing never says
- 31 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Build data-intensive systems that actually scale. Own distributed processing, not just services. Shape how large-scale data flows power products.
We’re looking for a Senior Scala / Data Engineer for one of our clients — a company operating in a data-heavy, distributed systems environment. This is a hands-on role for someone with a strong Scala background who has built and optimized high-performance data systems in production.
This role is for someone who takes full ownership of distributed data systems — from architecture to performance. You’ll work on complex, large-scale data processing challenges where efficiency, scalability, and fault tolerance are critical.
Your responsibilities will include:
• Build and optimize distributed data processing systems using Scala;
• Design and implement high-throughput, low-latency data pipelines;
• Work with big data frameworks (e.g., Spark) and distributed architectures;
• Develop and maintain scalable backend services for data-intensive workloads;
• Optimize performance of data processing jobs and infrastructure;
• Ensure fault tolerance and resilience of distributed systems;
• Implement monitoring, logging, and observability for data pipelines;
• Collaborate with engineering teams to design scalable system architectures;
• Contribute to cross-functional backend and data engineering efforts;
• Ensure high standards of code quality, performance, and reliability.
What we expect from you:
• Strong experience with Scala in production environments;
• Proven experience as a Senior Data Engineer or Backend Engineer in data-heavy systems;
• Strong understanding of distributed systems and parallel processing;
• Experience with big data technologies (Spark, Kafka, etc.);
• Strong knowledge of JVM ecosystem and performance tuning;
• Experience building high-load, scalable systems;
• Strong system design and architectural thinking;
• Ability to work independently and take ownership;
• Strong problem-solving skills in complex, distributed environments;
• English level: B2+ (C1 preferred);
• Availability to work in EU timezone.
Nice to have:
• Experience with functional programming paradigms;
• Experience with cloud infrastructure (AWS / GCP);
• Experience with stream processing frameworks (Flink, Akka Streams);
• Background in low-latency or real-time systems;
• Experience in niche or hard-to-hire tech domains.
We offer:
• Six-month full-time contract engagement;
• Remote work with overlap in US business hours;
• High-impact project with direct influence on production AI systems;
• Close collaboration with client’s product and engineering teams;
• Opportunity to build core infrastructure for real-world AI applications — not prototypes.
Originally posted on Himalayas
We’re looking for a Senior Scala / Data Engineer for one of our clients — a company operating in a data-heavy, distributed systems environment. This is a hands-on role for someone with a strong Scala background who has built and optimized high-performance data systems in production.
This role is for someone who takes full ownership of distributed data systems — from architecture to performance. You’ll work on complex, large-scale data processing challenges where efficiency, scalability, and fault tolerance are critical.
Your responsibilities will include:
• Build and optimize distributed data processing systems using Scala;
• Design and implement high-throughput, low-latency data pipelines;
• Work with big data frameworks (e.g., Spark) and distributed architectures;
• Develop and maintain scalable backend services for data-intensive workloads;
• Optimize performance of data processing jobs and infrastructure;
• Ensure fault tolerance and resilience of distributed systems;
• Implement monitoring, logging, and observability for data pipelines;
• Collaborate with engineering teams to design scalable system architectures;
• Contribute to cross-functional backend and data engineering efforts;
• Ensure high standards of code quality, performance, and reliability.
What we expect from you:
• Strong experience with Scala in production environments;
• Proven experience as a Senior Data Engineer or Backend Engineer in data-heavy systems;
• Strong understanding of distributed systems and parallel processing;
• Experience with big data technologies (Spark, Kafka, etc.);
• Strong knowledge of JVM ecosystem and performance tuning;
• Experience building high-load, scalable systems;
• Strong system design and architectural thinking;
• Ability to work independently and take ownership;
• Strong problem-solving skills in complex, distributed environments;
• English level: B2+ (C1 preferred);
• Availability to work in EU timezone.
Nice to have:
• Experience with functional programming paradigms;
• Experience with cloud infrastructure (AWS / GCP);
• Experience with stream processing frameworks (Flink, Akka Streams);
• Background in low-latency or real-time systems;
• Experience in niche or hard-to-hire tech domains.
We offer:
• Six-month full-time contract engagement;
• Remote work with overlap in US business hours;
• High-impact project with direct influence on production AI systems;
• Close collaboration with client’s product and engineering teams;
• Opportunity to build core infrastructure for real-world AI applications — not prototypes.
Originally posted on Himalayas