Senior Data Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 32d ago
The date the source published, not the day we noticed it (2026-08-14). Last seen at its source 2h ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Cyprus, Georgia, Poland, Spain, Serbia, Latvia
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $184.4k–260.8k/yr
Middle 50% of 8 listings that do state pay — Engineering · Senior · Georgia · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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workable employer's own board first seen 21d ago · last seen 2h ago
The listing
We are looking for a Senior Data Engineer with a strong software engineering background and deep expertise in building modern cloud-based data platforms.
You will be responsible for designing and developing scalable data infrastructure, building reliable data pipelines, and establishing engineering best practices across our data ecosystem. This role is ideal for someone who enjoys solving complex engineering challenges while enabling analytics, machine learning, and product teams with high-quality data.
Responsibilities
Data Platform
- Design, build, and evolve GoMining's cloud data platform on Google Cloud Platform.
- Develop scalable data architecture following modern engineering principles, including Medallion Architecture.
- Define and maintain data models, data contracts, and integration standards across systems.
Data Engineering
- Build and maintain reliable batch and streaming data pipelines.
- Integrate data from internal services and third-party APIs.
- Develop scalable ETL/ELT workflows and automate data processing.
- Build and maintain analytical data marts for business and product teams.
Platform Reliability
- Ensure data quality, consistency, and reliability across the platform.
- Implement monitoring, alerting, and SLA management for data pipelines.
- Continuously improve platform performance, scalability, and operational efficiency.
Engineering Excellence
- Drive Infrastructure as Code (IaC), CI/CD, and automation across the data platform.
- Contribute to the development of ML and AI infrastructure.
- Establish engineering best practices, code quality standards, and technical documentation.
- Collaborate closely with Analytics, Data Science, Product, and Engineering teams.
Technology Stack
- Python
- SQL
- Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Composer)
- Apache Airflow
- Terraform
- Kubernetes
- Docker
- CI/CD
- REST APIs
- Git
Requirements
- 6+ years of experience in Data Engineering, including 3+ years in Senior-level roles.
- Strong software engineering background with excellent Python skills.
- Proven experience designing and building cloud-based data platforms and data warehouses.
- Strong hands-on experience with Google Cloud Platform, including BigQuery, Dataflow, Pub/Sub, and Composer (Airflow).
- Experience building ETL/ELT pipelines, streaming architectures, and API integrations.
- Experience with Terraform, Kubernetes, Docker, and CI/CD pipelines.
- Strong understanding of data modeling, distributed systems, and scalable data architectures.
- Experience implementing data quality, monitoring, and observability solutions.
- Strong SQL skills
Nice to have
- Experience building ML infrastructure and supporting Data Science teams.
- Experience with Kafka or other streaming platforms.
- Experience working with fintech, crypto, Web3, or high-scale B2C products.
- Experience with modern data lakehouse architectures.
- Experience using AI tools to improve engineering productivity.
Benefits
- Professional growth: support for courses, conferences, and English learning (up to 100% coverage).
- Work-life fit: remote or hybrid format with flexible hours across international teams.
- Paid leave: up to 20 vacation days + 8 company holidays + 5 personal days per year
- Recognition programs: structured performance reviews and team awards.
- Team culture: retreats in international locations (for example, company apartments in Cyprus).