Senior Data Engineer

• 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

• 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.

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.

• 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.
• 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.
• 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.
• 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.

• Python
• SQL
• Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Composer)
• Apache Airflow
• Terraform
• Kubernetes
• Docker
• CI/CD
• REST APIs
• Git