Requirements
4+ years of professional experience in Data Engineering, Data Warehousing, Data Ops or a related field.Good hands on experience in one of the major clouds . AWS preferred, Expert Hands on experience required on linux toolsShould be able to deploy applications using docker on bare metal servers.Bachelor's degree or higher in Computer Science, Mathematics, Physics, Engineering, Economics, or another quantitative discipline.Strong expertise in SQL PSQL and relational database concepts, including data modeling and query optimization.Experience designing and maintaining data warehouse architectures.Hands-on experience with columnar databases and performance optimization techniques.Experience working with workflow orchestration and distributed job scheduling tools, preferably Apache Airflow.Solid programming skills in Python or Java, with a preference for Python.Good understanding of Linux environments and scripting (Bash).Experience with modern cloud-based data platforms and analytical databases such as ClickHouse, StarRocks, or similar technologies.Have experience working s3 compatible open source solution and managed solutions.Familiarity with data integration, CDC/ETL/ELT frameworks, and large-scale data processing.Strong communication skills in English.
Description
We are looking for a Senior Data and infrastructure Enginee to join our Data Team and help transform our company into a truly data-driven organization. In this role, you will design and build scalable data solutions that empower business teams with reliable, high-quality data and actionable insights.If you enjoy solving complex data challenges, building robust platforms, and working in an international and collaborative environment, we'd love to hear from you.Responsibilties:Design, develop, deploy and maintain scalable and open source data solutions that support our rapidly growing international business.Build and evolve our data Architecture to enable advanced analytics, reporting, and data-driven decision-making across the organization.Develop and optimize CDC / ETL/ELT pipelines for batch and near real-time data processing.Support analytical and machine learning workflows by providing reliable and well-structured datasets.Integrate data from a wide range of sources, including transactional databases, third-party platforms, and external APIs (e.g., PostgreSQL, MySQL, Google Analytics, BigQuery, Google Ads, Adjust, and others).Collaborate closely with data engineers, analysts, data scientists, and business stakeholders to ensure data quality, consistency, and governance standards.Monitor, troubleshoot, and continuously improve data platform performance, reliability, and scalability using managed and open source solutions.Evaluate and recommend new technologies (Open source/ Managed), tools, and architectural approaches to enhance our data ecosystem.Contribute to technical documentation, knowledge sharing, and best practices within the Data Team and the wider organization.