Principal Data Engineer

Vacancy details
Data Engineering
Data Engineering Lead
Lead
Bulgaria, 
Croatia, 
Poland, 
Portugal, 
Spain, 
Ukraine
Remote

Over 20 years of market experience, Intellias brings together technologists, creators and innovators in Europe, North and Latin America, and the Middle East. Join our international team and take the mission to solve the advanced tech challenges of tomorrow! 

What project we have for you

We are seeking an experienced Principal Data Engineer to lead a team in developing and maintaining robust, scalable data pipelines, bridging on-premises and cloud environments, and delivering real-time analytics systems. This role requires deep expertise in data engineering and streaming technologies, combined with strong leadership skills to drive the team towards achieving business objectives. The manager will collaborate with cross-functional teams including architecture, product, and software engineering to ensure the delivery of high-quality data solutions aligned with company goals.

What you will do

  • Lead, mentor, and manage a team of data engineers specializing in streaming technologies. 
  • Design and maintain scalable data storage solutions with ClickHouse for fast analytics on streaming data. 
  • Design and implement high-throughput, low-latency streaming data pipelines using Apache Kafka. 
  • Oversee the development of stream processing applications using Apache Spark or Apache Flink. 
  • Implement real-time data transformations and analytics using KSQL. 
  • Ensure integration between on-premises streaming solutions and cloud services (e.g., BigQuery, Looker). 
  • Lead the design and implementation of ETL processes, extracting, transforming, and loading data into a data warehouse. 
  • Ensure data integrity, consistency, and accuracy through robust data quality assurance. 
  • Optimize performance and implement best practices in data engineering, covering data quality, security, and efficiency. 
  • Collaborate with stakeholders to gather requirements, aligning data strategies with business objectives. 
  • Stay current with emerging technologies in both streaming and cloud environments, evaluating their potential application. 

What you need for this

  • 5+ years of hands-on data engineering experience, including expertise in Python, Scala, or Java. 
  • 3+ years of experience with cloud vendors (AWS, Azure, GCP), data warehouse services (e.g., Redshift, Databricks), and cloud storage (Azure Storage, S3). 
  • Familiarity with KSQL for real-time data analytics and ClickHouse for fast analytics on streaming data. 
  • Strong experience with ETL/ELT tools (e.g., Airflow, ADF, Glue, NiFi) and orchestration. 
  • Proficient in code versioning (Git) and CI/CD pipelines for data projects. 
  • Experience with stream processing tools like Apache Kafka, Apache Flink, or Apache Spark Structured Streaming. 
  • Experience with data modeling, optimization techniques, and stream processing patterns like windowing and exactly-once processing. 
  • Excellent leadership and mentorship skills, with at least 2 years in a leadership role. 

Nice to Have: 

  • Experience with NoSQL databases and stream processing tools such as Kafka Connect and Kafka Streams. 
  • Knowledge of Superset for data visualization. 
  • Experience integrating real-time machine learning models into streaming environments. 
  • Expertise with monitoring and observability tools for streaming systems across on-premises and cloud environments. 

What it’s like to work at Intellias

At Intellias, where technology takes center stage, people always come before processes. We’re dedicated to cultivating a tech-savvy environment that empowers individuals to unlock their true potential and achieve extraordinary results. Our customized benefits not only prioritize your well-being but also charge your professional growth, making this opportunity an ideal match for tech enthusiasts like you.

Have not found the most
suitable position
yet?

Leave your resume and we will select a cool option for you.
Good news!
Link copied
Good news!
You did it.
Bad news!
Something went wrong. Please try again.