Middle Data Engineer

Ternopil, Office/Hybrid

Full Time

As a Data Engineer, you will be responsible for designing, building, and maintaining scalable data pipelines and data infrastructure that support analytics and data-driven decision-making. You will work closely with Data Scientists, Analysts, and other engineering teams to integrate data from multiple sources, transform and process large datasets, and ensure reliable and efficient data availability for downstream applications and analytics

Requirements:

1+ years of experience in Data Engineering / Analytics

Strong SQL knowledge:

complex joins

window functions

aggregations

basic query optimization

Good Python skills for data processing, OOP

Understanding of ETL/ELT concepts

Experience working with parquet / json / csv formats

Basic understanding of Data Lake concepts

AWS:

Hands-on experience with Amazon Web Services (AWS)

Experience with:

Amazon S3 (buckets, lifecycle, permissions)

Amazon RDS

AWS IAM (roles & policies basics)

Amazon EC2

Basic understanding of VPC and security groups

As a plus:

Experience with Apache Spark

Experience with Apache Airflow

Familiarity with Docker, Docker-compose, Kubernetes

Experience with Databricks

Responsibilities:

Design and maintain data pipelines to ingest, transform, and load data from various internal and external sources into data warehouses or data lakes.

Develop and optimize data processing workflows for large-scale datasets using distributed processing frameworks and SQL-based transformations.

Ensure data quality, reliability, and monitoring by implementing validation, logging, and alerting mechanisms across data pipelines.

Collaborate with Data Scientists and Analysts to provide clean, well-structured datasets and support analytical and reporting use cases.