Senior Backend Engineer - Data
IONIX Tel-Aviv, Tel-Aviv District, Israel
Computer and Network Security · 51-200 employees
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About the role
Design, build, and operate large-scale data ingestion pipelines and services, including data modeling, normalization, quality monitoring, recovery, and data-lake foundations. Own features from data through customer-facing APIs and UI as needed, improve existing systems, address scale and performance issues, and lead technical initiatives while supporting production operations and on-call.
What they look for
Requirements
Requires 5+ years of production backend development and 3+ years building production data pipelines or data-intensive systems, with strong Python skills and experience ingesting data from external APIs at scale. Candidates should have experience with a modern data platform, cloud infrastructure, containers, Kubernetes, CI/CD, web and network protocols, end-to-end ownership, and hands-on use of AI coding tools; a bachelor's degree in a relevant field or equivalent experience is required.
Full description
Our customer base has grown fast, and our platform has to grow with it. We're looking for a senior backend engineer with a strong data-engineering background to own the pipelines and services that process data at scale across our platform. You'll take services from idea to production on your own: design, build, ship and run them, and you're not afraid to open old code and make it better.
About IONIX
IONIX External Exposure Management protects enterprises' external attack surface from cyber risks and increases security team efficiency by providing tools that shorten the time to discover and prioritize exposures. IONIX discovers every internet-facing asset, assesses dependencies and connections, and validates exploitable risks so teams can remediate the exposures that matter. Global leaders including BlackRock, Infosys, Sompo, The Telegraph and E.ON depend on IONIX to manage their complex and dynamic attack surface.
Responsibilities
- Design, build, and run ingestion pipelines that collect internet-scale data from diverse public and third-party sources, handling rate limits, retries, and backfills.
- Own data modelling: schemas, partitioning, and storage layout that hold up as volume grows.
- Clean, normalise and deduplicate data from many noisy sources into consistent, trustworthy entities.
- Make data reliable: quality checks, freshness monitoring, fail-loud ingestion, backfills and recovery.
- Take features end to end, from the data to the customer: expose APIs, wire the UI when needed, and change code owned by other teams.
- Make data ready for analysis: build the ingestion and data-lake foundations our data analysts work on.
- Work with analysts to automate data verification and cleaning, and replace manual processes with automated ones.
- Solve scale and performance problems across pipelines and services.
- Improve and modernise existing code, not only write new code.
- Lead technical initiatives and raise the bar for the team.
- Own your work in production. Take part in on-call, take initiative to cut alert noise and fix root causes, not only symptoms.
- Use AI tools and agents as a core part of how you build.
Requirements
- 5+ years of hands-on backend development on production systems at scale.
- Data-engineering mindset and experience - a must: 3+ years building production data pipelines or data-intensive systems, with your own data models and partitioning schemes. You treat data as the foundation of a product, not the end of the job. You make decisions based on data, not vague assumptions.
- Strong Python - a must.
- Hands-on experience with large-scale data ingestion, including consuming external APIs at volume (rate limits, pagination, retries, idempotency).
- Hands-on experience with a modern data platform (e.g., Databricks).
- Solid understanding of network and web protocols.
- Experience with cloud providers, containers, Kubernetes and CI/CD.
- Proven end-to-end ownership, from design to production. You're comfortable working across the stack (APIs, frontend) when a feature needs it.
- Daily, hands-on use of AI coding tools and agents.
- B.Sc. in Computer Science, Computer Engineering, or equivalent experience.
Nice to have
- Hands-on experience with Databricks and PySpark.
- Experience implementing data lineage and data-quality tooling.
- Experience scaling out SQL and NoSQL databases and their operations.
