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WHO WE ARE LOOKING FOR

An AI-fluent data engineer passionate about solving complex challenges and turning raw data into business value for clients across industries.

WORK MODEL:

Hybrid, Office

The AI & Data Engineering team is one of the newest additions at Infinum, focused on delivering impactful solutions in data engineering and artificial intelligence. We work on a range of client projects, helping turn data into real business value.

Our work spans everything from building robust data pipelines to developing AI-powered systems that extract insights and enable smarter decision-making. Right now, we’re building machine learning algorithms that recommend relevant content to users of an IoT app, along with data pipelines that power analytics and ML models behind the scenes.

As a growing team, we’re looking to significantly expand in the near future. We often collaborate with DevOps, Backend, QA, and System Architect teams and have delivered data-based solutions for industries like smart devices, hospitality, and healthcare. If you’re excited by complex challenges, enjoy collaborating across disciplines, and want your work to make a real impact in industries that matter, come build with us. We’re growing fast, learning faster, and always up for a cup of coffee and a great idea.

RESPONSIBILITIES:

• designing and implementing high-quality data architecture and software solutions.
• building and maintaining robust, scalable data pipelines.
• identifying, prioritizing and executing development tasks.
• automating tasks through appropriate tools and scripting.
• collaborating with other teams and vendors on enhancing products.
• documenting your work clearly and comprehensively.
• supporting technology adoption and integration into flagship products.
• working closely with the product team to research and develop ways for improving user experience, utilizing data and machine learning concepts.
• recommending and developing solutions that will create new product features or update existing ones, increase scalability, and eliminate technical debt.

QUALIFICATIONS & EXPERIENCE

• at least 2 years of experience as a Data Engineer.
• experience with scalable and production-ready distributed data systems (SQL/NoSQL) and cloud-based data solutions.
• proficiency in Python (preferred) for designing robust data solutions and AI integrations.
• experience with ETL tools and efficient, secure data pipelines.
• strong grasp of data lakehouse concepts, including modeling and performance.
• experience with Databricks or similar platforms for collaborative data and AI. development, ideally with at least one year of hands-on experience.
• familiarity with Apache Spark, Snowflake, or Microsoft Fabric.
• hands-on experience with AWS, Azure, or GCP for cloud-native infrastructures.
• advanced AI fluency: comfortable integrating AI tools into your daily workflow to speed up delivery.
• broad knowledge of design patterns for scalable, maintainable code.
• experience working independently and collaboratively in agile environments (Scrum, Kanban).
• excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• fluency in English.

Bonus points

• a master’s degree in computer science, data science, or mathematics.
• solid understanding of LLMs, prompt engineering, RAG architectures, and other similar AI concepts.
• familiarity with integrating pipelines with AI models and deploying AI features.
• knowledge of vector databases, graph databases, and experience with real-time streaming pipelines.
• exposure to Kafka and Kubernetes.

TOOLS WE USE

• Python, SQL, Pandas, NumPy & PySpark - Python is our primary language for data processing, complemented by Pandas and NumPy for efficient data wrangling and analysis. PySpark allows us to scale data processing tasks seamlessly. SQL remains essential for querying and transforming data across platforms.
• Apache Airflow - Our tool of choice for orchestrating data workflows. Airflow schedules, monitors, and manages complex pipelines, keeping data flowing reliably.
• Azure Data Factory, AWS Glue, GCS & DataFlow - We use these managed services to build, deploy, and manage data pipelines across cloud platforms, ensuring smooth data integration and transformation.
• BigQuery - Google Cloud’s serverless data warehouse powers our fast, scalable SQL queries on large datasets, without the need to manage infrastructure.
• Databricks & Snowflake - Databricks offers a collaborative environment for big data and AI, while Snowflake provides a flexible and powerful data warehousing solution.
• Delta Lake & Iceberg - We rely on these technologies to manage large-scale data lakes with ACID transactions and schema evolution, making our pipelines reliable and performant.
• Docker & Git - Docker simplifies local development and containerized deployments. Git (with GitHub or Bitbucket) supports our pull request workflows and Continuous Integration practices.