As a Senior Data Engineer, you’ll be joining a scale-up environment where adaptability and problem-solving are key. This role is essential in designing, building, and optimizing our data architecture to support evolving business needs. With expertise in data pipeline stacks (open-source & AWS) and ClickHouse, you’ll focus on developing scalable, high-performance solutions. Collaborating closely with engineering teams, you’ll implement robust data pipelines that ensure efficiency, reliability, and alignment with business objectives. If you’re looking for a role where you can make a real impact, we’d love to hear from you.

REQUIREMENTS:

• 5+ years of experience in data engineering with solid experience on open-source data stack, and cloud native experiences.
• Expertise in ClickHouse, or similar OLAP databases is a plus.
• Strong experience in designing and architecting large-scale data systems.
• Proficiency in programming languages like Python, or productivity libraries scripting languages for data processing and pipeline development.
• Deep understanding of ETL/ELT processes, data modeling (Star Schema, Snowflake Schema), and data warehousing.
• Experience with orchestration tools such as Apache Airflow, Step Functions, or Dragster.
• Hands-on experience with infrastructure-as-code (Terraform, CloudFormation, CDK) is a plus
• Strong problem-solving skills and ability to work in a fast-paced environment.
• Knowledge of SQL query performance tuning, materialized views, and sharding strategies for large datasets.
• Familiarity with containerization and serverless computing (Docker, Kubernetes).
• Experience with monitoring and observability tools such as Prometheus, Grafana, AWS CloudWatch.

• Data pipeline Design: Robust design experience in follow and implement scalable, high-performance data architectures using AWS services and ClickHouse (or equivalent OLAP databases).
• Data Pipeline Development: Design, build, and maintain robust ETL/ELT pipelines that efficiently handle large-scale data ingestion, transformation, and storage.
• Cloud Infrastructure: Combine open-source data stack and AWS technologies to build and optimize data workflows.
• Data Governance & Quality: Ensure data accuracy and consistency through best practices in data governance, lineage, and monitoring.
• Performance Optimization: Optimize data storage, retrieval, and processing to support high-performance analytical workloads using partitioning, indexing, and query optimization techniques.
• Collaboration & Leadership: Work closely with data scientists, analysts, and software engineers to understand requirements and deliver data-driven solutions, mentoring junior engineers.
• Automation & CI/CD: Implement automated data pipeline deployment and monitoring strategies.