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Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.
We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.
We’re looking for a Senior Data Engineer to join our growing Data & Analytics practice and build modern data solutions for our customers on AWS.
This is a highly hands-on engineering role. You’ll work across different customer environments and data challenges, from modernizing legacy pipelines and building lakehouse platforms to optimizing analytical workloads and owning smaller data platforms end-to-end.
You won’t be limited to one stack or one long-running product. One project may involve Spark and Apache Iceberg, another Redshift performance and data modeling, and another Kafka, OpenSearch, or a new data platform built from ingestion through analytics.
We’re looking for someone who can take ownership of a data engineering problem, make sound technical decisions, and deliver independently without needing constant direction.
📍 Work location: remote from Ukraine
Curious about what it's really like to work at Automat-it?
Explore our benefits, culture, and what success in your first year could look like here.
If you are interested in this opportunity, please submit your CV in English.
What you'll do
Design, build, and optimize production data pipelines and data platforms on AWS.
Build scalable data processing workloads using Spark/PySpark, Python, and SQL.
Build and modernize data lakes and lakehouse architectures, working with technologies such as Amazon S3, Apache Iceberg, AWS Glue, and related data services.
Design, troubleshoot, and optimize analytical workloads and data warehouses, including Amazon Redshift and other OLAP platforms.
Modernize legacy and inefficient data workloads, including moving existing ETL/ELT processing to scalable distributed data architectures.
Work with streaming and event-driven data pipelines using technologies such as Kafka, Kinesis Data Firehose, and related services.
Investigate real production data problems, from performance bottlenecks and data modeling issues to upstream/downstream dependencies, and recommend practical solutions.
Take ownership of customer projects end-to-end, from understanding the technical problem through implementation, testing, and delivery.
Work directly with customers when needed to clarify requirements, explain technical decisions, and recommend solutions based on your expertise.
Build data foundations for analytics and ML workloads, collaborating with Data Scientists, DevOps, and MLOps engineers.
Contribute to dashboards and analytics when required using QuickSight or other BI tools.
Apply practical engineering practices around data quality, testing, CI/CD, security, governance, and infrastructure automation.
Requirements
Strong production experience in Data Engineering, with the ability to independently own and deliver data projects.
Deep hands-on experience with Apache Spark / PySpark and distributed data processing.
Strong Python and SQL skills.
Hands-on experience building data solutions on AWS.
Strong understanding of data lakes, lakehouse architectures, ETL/ELT, data modeling, and data processing at scale.
Experience with analytical databases and data warehouses. Strong Amazon Redshift experience is highly valuable; deep experience with platforms such as Snowflake, Synapse, or similar OLAP technologies is also relevant.
Understanding of streaming architectures and technologies such as Kafka or Amazon Kinesis.
Ability to troubleshoot existing data systems, understand how data flows across upstream and downstream components, and improve performance and reliability.
Ability to work autonomously, make pragmatic technical decisions, and take responsibility for delivery.
Good communication skills and the ability to discuss technical problems and solutions directly with customers in English.
Nice to have
Experience with Apache Iceberg or other modern open table formats and lakehouse technologies.
Amazon OpenSearch Service / Elasticsearch experience.
Experience with BI and visualization tools such as Amazon QuickSight, Tableau, or Power BI.
Terraform and Infrastructure as Code.
CI/CD practices for data pipelines, including data testing and validation.
Experience preparing data platforms for ML workloads using services such as Amazon SageMaker.
Exposure to Amazon Bedrock, GenAI, or agentic development.
Experience with Databricks, Snowflake, ClickHouse, or other modern data platforms
Automat-it is committed to fostering a workplace that promotes equal opportunities for all. We firmly believe that cultivating a diverse workforce is crucial to our success. Our recruitment decisions are grounded in your experience and skills, recognizing the value you bring to our team.