Senior Data Engineer (Google Cloud Platform)

Senior Data Engineer (Google Cloud Platform) (EN)

About the project:

We’re looking for a Senior Data Engineer with strong Google Cloud Platform expertise to join the centralized Data & AI team of a global organization operating across multiple business units and operating companies. The company is building scalable, reusable data engineering foundations - standardized pipelines, data products, and shared infrastructure - to support enterprise-wide data management, process automation, advanced analytics, and future AI initiatives.

The existing data infrastructure is built primarily on GCP, with BigQuery as the central data lake and analytical platform. The long-term vision is to build an internal AI Factory that delivers reusable data and AI solutions across the organization.

This is a hands-on engineering role, not a business analysis or data science position. The engagement is full-time and long-term (12+ months), with an expected start in mid-January 2027.

Your team:

You’ll join an established data engineering function within the centralized Data & AI team. You’ll work closely with the Principal Data Architect, who will introduce you to the existing architecture, as well as with other data engineers and business-focused AI specialists. As demand grows across operating companies, the team may expand.

Culture:

We uphold high standards in the tech industry and continuously evolve to meet them. Every team member can influence the product, the company, internal processes, and their own professional growth. We also regularly organize team-building activities to keep a friendly atmosphere and simply enjoy time together.

Technical stack:

• Languages: Python, advanced SQL

• Data platform: Google BigQuery (data lake), Google Cloud Dataflow, Google Cloud Composer, Google Dataform

• Streaming & integration: Google Cloud Pub/Sub, Apache Kafka (managed), Change Data Capture (CDC)

• Additional services: Google Cloud Workflows

• Practices: ETL/ELT pipelines, reusable ingestion and transformation components, batch, micro-batch, and streaming processing

Your responsibilities:

• Design, implement, and maintain scalable data ingestion and transformation pipelines

• Develop reusable pipeline patterns and components for use across multiple business units

• Support batch, micro-batch, and streaming data processing

• Build reliable ingestion processes for data from enterprise systems and external sources

• Implement data cleansing, transformation, and deduplication

• Develop and maintain solutions on BigQuery, Dataflow, Composer, and Dataform

• Implement or support CDC pipelines for relational databases and other transactional systems

• Build custom data processing components when managed services are not enough

• Optimize pipelines for performance, reliability, and maintainability; troubleshoot failures and data processing issues

• Support the creation of reliable datasets and data products for analytics, business applications, and AI solutions

• Collaborate with the Principal Data Architect to implement architectural standards and improve existing engineering patterns

• Work with teams across operating companies when centralized engineering support is needed

It’s about you:

• Strong hands-on experience designing and implementing data engineering solutions on Google Cloud Platform

• Practical experience with BigQuery and Dataflow

• Experience with workflow orchestration, preferably Cloud Composer

• Experience with SQL-based transformation frameworks, preferably Dataform

• Strong Python programming skills and advanced SQL

• Experience building production-grade ETL/ELT pipelines and reusable data ingestion and transformation components

• Knowledge of data quality, cleansing, deduplication, and transformation practices

• Understanding of cloud data lake architectures and enterprise data platforms

• Experience with batch and near-real-time processing; understanding of event-driven architectures

• Familiarity with Pub/Sub or Kafka; knowledge of CDC approaches for SQL databases

• Experience integrating data from heterogeneous enterprise systems

• Ability to work independently within an established engineering environment and follow architectural standards

• Ability to document technical decisions and communicate engineering trade-offs

• Good spoken and written English for daily communication with the client

• Time zone: CET preferable; overlap with US Eastern time is a plus

Would be a plus:

• Experience with managed Kafka services

• Experience implementing CDC pipelines in production

• Familiarity with Google Cloud Workflows

• Experience designing data products consumed by AI/ML applications

• Experience in enterprise environments with multiple operating companies or business units

• Experience supporting shared data engineering platforms

• Familiarity with infrastructure automation, CI/CD, and data pipeline monitoring

• Experience contributing to enterprise AI or advanced analytics initiatives

What we can offer you:

• Long-term engagement (12+ months) on a strategic initiative: building the data foundation of an enterprise AI Factory

• Work with a modern GCP data stack and an experienced architect on the client side

• Opportunities for professional and career growth through challenging tasks, meetups, hackathons, and professional communities

• Horizontal connections and no micromanagement: all team members are accessible to each other for any questions

• Hubs in Kharkiv, Kyiv, Lviv, and Wrocław (Poland), or everything necessary for remote work

• Up to 50% compensation for educational courses and conferences

• Free English and business English courses

• 20 days off and 7 sick days

• Legal and accounting support

• Appreciation gifts for significant events and occasions

How to join Techstack:

1. Pre-screening with a Talent Acquisition Specialist

2. English check

3. Technical screening and evaluation with Techstack engineers

4. Interview with the client (a second interview is possible)