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

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 8 points (from 67 before penalties). Reviewed 21 Sep 2026.