Senior Data Engineer

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.ResponsibilitiesData PlatformDesign, build, and evolve GoMining’s cloud data platform on Google Cloud PlatformDevelop scalable data architecture following modern engineering principles, including Medallion ArchitectureDefine and maintain data models, data contracts, and integration standards across systemsData EngineeringBuild and maintain reliable batch and streaming data pipelinesIntegrate data from internal services and third-party APIsDevelop scalable ETL/ELT workflows and automate data processingBuild and maintain analytical data marts for business and product teamsPlatform ReliabilityEnsure data quality, consistency, and reliability across the platformImplement monitoring, alerting, and SLA management for data pipelinesContinuously improve platform performance, scalability, and operational efficiencyEngineering ExcellenceDrive Infrastructure as Code (IaC), CI/CD, and automation across the data platformContribute to the development of ML and AI infrastructureEstablish engineering best practices, code quality standards, and technical documentationCollaborate closely with Analytics, Data Science, Product, and Engineering teamsTechnology StackPythonSQLGoogle Cloud Platform (BigQuery, Dataflow, Pub/Sub, Composer)Apache AirflowTerraformKubernetesDockerCI/CDREST APIsGitRequirements6+ years of experience in Data Engineering, including 3+ years in Senior-level rolesStrong software engineering background with excellent Python skillsProven experience designing and building cloud-based data platforms and data warehousesStrong hands?on experience with Google Cloud Platform, including BigQuery, Dataflow, Pub/Sub, and Composer (Airflow)Experience building ETL/ELT pipelines, streaming architectures, and API integrationsExperience with Terraform, Kubernetes, Docker, and CI/CD pipelinesStrong understanding of data modeling, distributed systems, and scalable data architecturesExperience implementing data quality, monitoring, and observability solutionsStrong SQL skillsNice to haveExperience building ML infrastructure and supporting Data Science teamsExperience with Kafka or other streaming platformsExperience working with fintech, crypto, Web3, or high-scale B2C productsExperience with modern data lakehouse architecturesExperience using AI tools to improve engineering productivityBenefitsProfessional growth: support for courses, conferences, and English learning (up to 100% coverage)Work-life fit: remote or hybrid format with flexible hours across international teamsPaid leave: up to 20 vacation days + 8 company holidays + 5 personal days per yearRecognition programs: structured performance reviews and team awardsTeam culture: retreats in international locations (for example, company apartments in Cyprus)#J-*****-Ljbffr

This role is ideal for someone who enjoys solving complex engineering challenges while enabling analytics, machine learning, and product teams with high-quality data.ResponsibilitiesData PlatformDesign, build, and evolve GoMining’s cloud data platform on Google Cloud PlatformDevelop scalable data architecture following modern engineering principles, including Medallion ArchitectureDefine and maintain data models, data contracts, and integration standards across systemsData EngineeringBuild and maintain reliable batch and streaming data pipelinesIntegrate data from internal services and third-party APIsDevelop scalable ETL/ELT workflows and automate data processingBuild and maintain analytical data marts for business and product teamsPlatform ReliabilityEnsure data quality, consistency, and reliability across the platformImplement monitoring, alerting, and SLA management for data pipelinesContinuously improve platform performance, scalability, and operational efficiencyEngineering