Do we have a match?
As a Data Engineer, you must meet the following criteria:
- Strong Python experience: data structures, typing, error handling, generators/iterators, context managers, and the standard library
- Software engineering fundamentals: modular design, dependency management, packaging, and API design
- Testing discipline: pytest, fixtures, mocking, and writing code that's testable by construction
- Hands-on experience building and operating ETL/ELT pipelines in production, not just scripts or notebooks
- Strong experience with Snowflake and dbt
- Experience with Apache Airflow or similar code-based orchestration tools
- Solid working knowledge of SQL and data modeling, sufficient to design robust database schemas and query them effectively
- Experience with Docker, Kubernetes, and CI/CD practices
- Debugging and profiling skills; able to reason about performance, concurrency, and memory in Python
- Experience with at least one public cloud (AWS or Azure)
- Experience with version control systems (Git)
- Able to explain technical trade-offs to both technical and business audiences
- Experience using AI coding assistants such as Claude Code, Cursor, or similar on a daily basis
- Strong communication skills and good knowledge of English (minimum C1 level)
Beyond the criteria above, we would appreciate the following nice-to-haves:
- Experience with Apache Spark, ideally on Databricks
- Experience with Pydantic or similar schema-validation libraries
- Python web/API frameworks (FastAPI, Flask)
- Async Python, multiprocessing, or other concurrency patterns
- Experience with Azure AI Search or AWS OpenSearch
- A second language: Go, Rust, Scala, or TypeScript
- Familiarity with LLMs, Azure OpenAI, or agentic AI systems
Get to know us better
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’ve kept our people-oriented culture. Our values are simple:
- Act to deliver.
- Disrupt to grow.
- Team up to win.
The project and the team
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:
- Languages: Python, SQL
- Data platform: Snowflake, dbt
- Workflow orchestration: Apache Airflow (complex DAGs), running on Kubernetes
- Data processing: Apache Spark on Azure Databricks
- Data tooling and DBs: pandas, Polars, PyArrow, DuckDB, PySpark, PostgreSQL, Redis
- Cloud: Azure (AKS, Blob Storage, ACR, Databricks, OpenSearch, Azure AI Search)
- API & services: FastAPI (REST, async), API Gateway
- Testing & code quality: pytest, mypy/pyright, ruff/black, sqlfluff, SonarQube
- Schema validation: Pydantic
- Dependency & environment management: uv, Poetry
- CI/CD & infrastructure: GitHub Actions, Docker, Kubernetes
- AI-Assisted Development: Cursor, Claude Code, ChatGPT Enterprise
- Future direction: agentic AI systems, LangChain, Azure OpenAI integration
What else you should know:
- Team: Data Architecture Lead, Data Engineers, DataOps Engineers, Backend Engineer, Product Owner, collaboration with Frontend Engineers and Data Science and AI Engineers
- Distributed team across Europe and India
- Agile, collaborative environment; given the platform's organization-wide impact, we're looking for a mature, proactive, results-driven approach
- Code quality is enforced through testing, typing, and tooling - not just code review
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.
More reasons to join us
- Flexible working hours and approach to work: fully remotely, in the office or hybrid
- Professional growth supported by internal training sessions and a training budget
- Solid onboarding with a hands-on approach to give you an easy start
- A great atmosphere among professionals who are passionate about their work
- The ability to change the project you work on
,[Engineering & delivery (~70%), Design, build, and maintain batch and streaming data pipelines in Python, including their orchestration, scheduling, and monitoring (Airflow), Write reusable, well-typed Python libraries and internal packages used by other engineers and analysts, Build Python services and APIs (FastAPI) that expose data to downstream applications, and integrate with third-party and internal APIs, Develop data transformation logic in SQL and dbt on Snowflake, and build data models that support fast, reliable querying, Write unit, integration, and data-contract tests, and keep pipelines covered by automated CI, Profile and optimize Python code and data processing jobs for runtime, memory, and cost, Deploy and operate code in the cloud using containers, infrastructure as code, and CI/CD, Enforce access controls, secrets handling, and sensitive-data protections throughout the data lifecycle, Using AI coding assistants effectively while validating all generated output before it reaches production and helping build automated quality gates, Continuous improvement (~30%), Find and fix efficiency, reliability, cost, and correctness issues in existing pipelines, refactoring toward simpler designs, Replace one-off scripts and notebooks with tested, packaged, scheduled code, Implement data quality checks, validation, and monitoring that catch issues before consumers do, Create matching logic to deduplicate and connect entities across multiple data sources, Document data processes and system architecture, and maintain project documentation] Requirements: Python, Data structures, API, pytest, ETL, Snowflake, Apache Airflow, SQL, Data modeling, Docker, Kubernetes, Public cloud, Version control system, AI, Communication skills, Apache Spark, Web Api, FastAPI, Azure, AWS, Go, Rust, Scala Additionally: Sport subscription, Training budget, Private healthcare, Flat structure, Small teams, International projects, Masterclazz training, Free coffee, Bike parking, Playroom, Free beverages, Free lunch, In-house trainings, Modern office, No dress code.