Data Engineer (Medior/Senior)
At a glance
Senior Data Engineering role at SmartCat. Remote · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain Spark pipelines that move large datasets through modern data platforms
- Shape lakehouse structures for analytics and machine learning, then tune them for efficient use
- Create data models that keep information accurate, consistent and accessible
- Work with analysts and data scientists to turn their data needs into workable solutions
- At senior level, guide junior engineers and help improve pipeline reliability and engineering practices
What you bring
- You work with Python or Scala/Java and have advanced SQL skills
- You have practical experience with Spark and at least one of Databricks, Snowflake or Microsoft Fabric
- You understand lakehouse design, distributed systems, data modeling and warehousing
- You have built cloud-native data architectures on AWS, Azure or GCP
Who this fits
This role suits a medior or senior engineer who enjoys learning and trying different approaches, and values a healthy work-life balance. Senior-level work includes mentoring junior engineers. The role is remote with flexible working hours.
From the employer
SmartCat is an AI-driven company that tackles complex data challenges with innovative solutions. Our expert team collaborates on carefully selected projects that deliver measurable, positive impacts on businesses, everyday life, and society.
Position Overview:
We are seeking a talented Medior/Senior Data Engineer to join our team. In this role, you will build and optimize our data lakehouse infrastructure using Spark and Databricks, designing and implementing robust, scalable data pipelines and ensuring the efficient processing and storage of large datasets.
Key Responsibilities:
- Design, build, and maintain scalable data pipelines using Apache Spark and modern data platforms.
- Develop and optimize data lakehouse architectures to support data analytics and machine learning workflows.
- Implement data models and structures that ensure data quality, consistency, and accessibility.
- Collaborate with data scientists and analysts to understand data requirements and deliver solutions.
- Help ensure data security and compliance with relevant regulations.
- Monitor and troubleshoot data pipeline performance and reliability.
- Continuously improve data engineering processes and best practices.
- Collaborate with cross-functional teams and contribute to technical discussions.
- At the Senior level, you'll also provide technical guidance and mentor junior engineers.
Requirements:
- Experience with Python or Scala/Java.
- Advanced knowledge of SQL.
- Hands-on experience with Apache Spark, Databricks, Snowflake, or Microsoft Fabric.
- Understanding of data lakehouse concepts and architecture.
- Grasp of distributed systems fundamentals.
- Data modeling and data warehousing skills.
- Experience with cloud-native data architectures on AWS, Azure, or GCP.
- You enjoy learning new things and trying different solutions.
- You value a healthy work-life balance; we don't like overtime.
- You know when and how to ask for help; we work together as a team.
Nice to have:
- Kafka, Flink, or similar streaming technologies.
- Kubernetes.
- Machine Learning.
What we offer:
- Clearly defined pay grades: from L1 (talented junior) to L10 (principal)
- The career path that connects these grades — you know where your life is going (at least here with us)
- Loyalty coefficient: 10% on net compensation after 3 years in SmartCat, 20% after 5 years
- Knowledge budget
- Flexible working hours and work from home
- We are transparent — from L-levels to salary, strategy, financial reports, and beyond
- You will have our full support towards gaining expertise
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