Senior Data Engineer (Snowflake) @ Link Group

Link Group

Kraków

On-site

PLN 120,000 - 180,000

Full time

14 days+

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Job summary

Link Group is seeking an experienced Test Automation Engineer / Automation Quality Engineer to modernise and optimize the test automation landscape. You will reduce test execution time, raise release confidence, and enhance GUI regression coverage while migrating test pipelines to GitHub Actions.

The role is hands-on, collaborating with backend/platform teams and leveraging AI tools to identify gaps, troubleshoot issues, and speed up engineering work.

Qualifications

Hands-on experience with Snowflake data modelling, performance tuning, cost optimisation, and security/governance.
Proven experience building production-grade ELT/ETL pipelines using dbt, Apache Airflow, Azure Data Factory, or similar.
Expert-level SQL skills.
Proficiency in Python for automation and data processing.
Experience using AI-assisted development tools to improve speed, quality, and documentation.
Strong data quality mindset with testing, monitoring, alerting, observability.
Ability to own initiatives from unclear requirements to reliable data products.
Strong communication skills to explain data topics to business stakeholders.

Responsibilities

Build and maintain reliable ELT/ETL pipelines that move data from transactional systems, microservices, event streams, and external sources into Snowflake.
Design clean, performant, and scalable data models for reporting, analytics, and customer-facing insights.
Contribute to the architecture of the end-to-end data platform, from ingestion and transformation to serving and analytics.
Apply modern data modelling practices, with a focus on modular, tested, and well-documented models.
Use AI development tools such as Claude and Cursor to accelerate pipeline development, query optimisation, documentation, and quality improvements.
Implement data quality checks, automated testing, monitoring, and observability into data pipelines from the start.
Support code reviews, documentation, and knowledge sharing around Snowflake, data modelling, performance optimisation, and data platform best practices.
Work closely with Software Engineers, Product Managers, Finance stakeholders, and analytics teams to translate data needs into reliable data products.

Skills

SQL

Python

Communication skills

Stakeholder management

Finance domain knowledge

Tools

Snowflake

ELT/ETL

dbt

Apache Airflow

Azure Data Factory

Power BI

DAX

Semantic models

Power Query

Microsoft Fabric

Azure

AWS

Azure Synapse

Azure Data Lake

AWS Glue

Kafka

Azure Event Hub

Job description

Strong hands-on experience with Snowflake, including data modelling, performance tuning, cost optimisation, and security/governance features.
Proven experience building production-grade ELT/ETL pipelines using tools such as dbt, Apache Airflow, Azure Data Factory, or similar.
Expert-level SQL skills.
Working knowledge of at least one general-purpose programming language, preferably Python, for automation and data processing.
Practical experience using AI-assisted development tools such as Claude or Cursor to improve speed, quality, and documentation.
Strong data quality mindset, including testing, monitoring, alerting, and observability.
Ability to take ownership of well-scoped initiatives from unclear requirements to reliable, adopted data products.
Strong communication skills and ability to explain technical data topics to business stakeholders.

Nice to have

Experience with Power BI, including semantic models, DAX, Power Query, report design, row-level security, incremental refresh, and Power BI Service.
Experience in finance, accounting, fintech, or another regulated environment.
Familiarity with Microsoft Fabric, including data mirroring from Azure-hosted sources.
Experience with cloud-native data architectures in Azure or AWS, such as Azure Synapse, Azure Data Lake, AWS Glue, or S3-based data lake patterns.
Experience with streaming or event-driven ingestion using Kafka, Azure Event Hubs, or similar technologies.
Understanding of Responsible AI principles and data infrastructure supporting auditable AI/ML pipelines.
Interest in modern data stack trends, open-source tooling, or emerging analytics engineering practices.

We are looking for an experienced Test Automation Engineer / Automation Quality Engineer to improve and modernise an existing test automation landscape. The role is focused on reducing test execution time, increasing release confidence, improving GUI regression coverage, and supporting the migration of test pipelines to GitHub Actions.

This is a hands-on role for someone who understands test automation at scale, can work close to backend/platform teams, and is comfortable using AI tools to identify gaps, troubleshoot issues, and speed up engineering work.

Responsibilities

Build and maintain reliable ELT/ETL pipelines that move data from transactional systems, microservices, event streams, and external sources into Snowflake.
Design clean, performant, and scalable data models for reporting, analytics, and customer-facing insights.
Contribute to the architecture of the end-to-end data platform, from ingestion and transformation to serving and analytics.
Apply modern data modelling practices, with a focus on modular, tested, and well-documented models.
Use AI development tools such as Claude and Cursor to accelerate pipeline development, query optimisation, documentation, and quality improvements.
Implement data quality checks, automated testing, monitoring, and observability into data pipelines from the start.
Support code reviews, documentation, and knowledge sharing around Snowflake, data modelling, performance optimisation, and data platform best practices.
Work closely with Software Engineers, Product Managers, Finance stakeholders, and analytics teams to translate data needs into reliable data products.

Requirements