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CodiLime is seeking a data engineer to join the team behind a large-scale data platform. You will design, code, test, and operate production Python systems—pipelines, libraries, and services—that move and transform data at scale.
You will work with Snowflake, dbt, Airflow, and Spark on Azure Databricks, building reliable, maintainable software powering enterprise datasets. This role offers remote/hybrid options and opportunities to grow within a collaborative Europe-India distributed team.
CodiLime is a software and network engineering industry expert and the first-choice service partner for top global networking hardware providers, software providers and telecoms. We create proofs-of-concept, help our clients build new products, nurture existing ones and provide services in production environments. Our clients include both tech startups and big players in various industries and geographic locations (US, Japan, Israel, Europe).
While no longer a startup - we have 250+ people on board and have been operating since 2011 we’ll keep our people-oriented culture. Our values are simple:
You will join the team behind a large-scale, centralized data platform built for a global consulting organization. The platform is the shared source of company data behind several of the firm’s internal products, and is used daily by consultants for company research.
This is fundamentally a data engineering role combined with software engineering: you’ll be designing, coding, testing, and operating production Python systems - pipelines, libraries, and services - that move, transform, and serve data at scale. This is not a role focused on configuring tools or writing one-off queries. You’ll be building reliable, maintainable software that powers our data platform. The goal is a unified, enterprise-grade dataset of 300M+ company records, integrated from 10+ external and internal sources.
The platform delivers firm-level and site-level data - firmographics, technographics, and hierarchical relationships (parent company, subsidiary, site) - alongside key business metrics such as revenue, CAGR, EBITDA, headcount, M&A activity, competitors, industry classification, and web traffic. Data needs to stay accurate, well-structured, and fast to query as both the dataset and the number of consumers keep growing.
Technology stack:
What else you should know:
We work on multiple interesting projects at a time, so it may happen that we’ll invite you to an interview for another project if we see that your competencies and profile are well suited for it.