Staff Engineer (Data Engineering)

TL;DR

Staff Engineer (Data Engineering): Building, evolving, and scaling a Data Platform to support Analytics, Data Science, ML Ops, and Product development, with an accent on reliability, self-service data workflows, and long-term architectural flexibility. Focus on designing shared platform capabilities, solving complex cross-domain challenges, and defining platform-wide standards for observability, data quality, and schema evolution.

Location: Hybrid in Vilnius, Lithuania. Opportunity to work from anywhere in the world for up to 30 days per year.

Salary: €7,400–€9,400 gross per month.

Company

hirify.global is transforming Europe’s €25B+ used car parts market, connecting 6,000+ scrapyard owners and sellers with millions of buyers through its marketplace, SaaS, and logistics platform.

What you will do

• Own and evolve core parts of the Data Platform, covering batch and streaming ingestion, processing, and delivery across product and analytics use cases.
• Drive architectural decisions for a modern, vendor-agnostic Data Platform, clearly separating ingestion, storage, compute, and serving layers.
• Act as a technical partner across teams (product, infrastructure, analytics), shaping scalable data and platform practices company-wide.
• Solve complex, cross-domain challenges at the intersection of software engineering, infrastructure, and data systems.
• Define and maintain platform-wide standards and guardrails (observability, data quality, schema evolution, metadata, access control).
• Act as a technical authority for high-impact platform and data decisions beyond a single team’s scope.

Requirements

• Strong hands-on experience building and operating large-scale data platforms, with solid data engineering fundamentals and real-world scalability experience.
• Hands-on experience running production-grade streaming systems (Kafka, Flink, Spark Streaming), including capacity planning, partitioning, and incident response.
• Experience designing and operating distributed services as part of a data platform, applying strong software engineering practices beyond data pipelines.
• Hands-on experience operating data platform infrastructure across Kubernetes (on-prem) and cloud environments (AWS, GCP, Snowflake), with a strong automation mindset.
• Experience owning ML workloads (feature store, model (re)training, model deployment, ML Ops, and monitoring) across batch and streaming systems.

Nice to have

• Deep experience with Parquet and Apache Iceberg, including metadata optimization, catalog integration, schema evolution, and large-scale table operations.
• Experience designing event-driven systems with clear delivery semantics, replay strategies, and correctness guarantees across batch and streaming.
• Strong experience building production-grade services in Go, Scala, or Rust.
• Experience with data modeling and transformations (e.g., dbt), emphasizing shared ownership, data quality, and long-term maintainability.
• Exposure to multimodal data processing (tabular, images, text, etc.), including specific data formats such as vector databases and data embeddings.
• Experience applying data governance, security, and compliance in large-scale platforms (ISO 27001/27701, SOC 2, GDPR).

Culture & Benefits

• Learning budget for personal and professional growth.
• Private health insurance.
• Employee stock option plan.
• Work from anywhere in the world for up to 30 days per year.
• Close collaboration with ambitious colleagues & a real opportunity to shape the “big picture”.
• Top-notch hardware and software (MacOS or Windows to choose from).
• Flexible working hours & remote work opportunities.