Senior Data Engineer

We are looking for a talented Senior Data Engineer to help build and enhance the data platform that supports analytics, operations, and data-driven decision-making across the organization.

You will work hands-on to develop scalable data pipelines, improve data models, ensure data quality, and contribute to the continuous evolution of our modern data ecosystem.

Responsibilities:

• Build, maintain, and optimize data pipelines for batch and streaming workloads.
• Develop reliable data models and transformations to support analytics, reporting, and operational use cases.
• Integrate new data sources, APIs, and event streams into the platform.
• Implement data quality checks, testing, documentation, and monitoring.
• Write clean, performant SQL and Python code.
• Contribute to improving performance, scalability, and cost-efficiency across the data platform.

Collaboration & Teamwork:

• Work closely with other senior engineers and architects to implement architectural patterns and best practices.
• Collaborate with analysts and data scientists to translate requirements into technical solutions.
• Participate in code reviews, design discussions, and continuous improvement initiatives.
• Help maintain clear documentation of data flows, models, and processes.

Platform & Process:

• Support the adoption and roll-out of new data tools, standards, and workflows.
• Contribute to DataOps processes such as CI/CD, testing, and automation.
• Assist in monitoring pipeline health and resolving data-related issues.

Requirements:

• 6-10 years of experience as a Data Engineer or in a similar role.
• Hands-on experience with Snowflake, including SQL development, data modeling, performance optimization, and administration of analytical workloads.
• Hands-on experience designing, building, and operating modern data pipelines and cloud-based data platforms.
• Experience with modern analytical data architectures, including data lakes, data warehouses, and open table formats such as Apache Iceberg.
• Experience with dbt (or similar tools), including model development, testing, documentation, and version control workflows.
• Exposure to streaming/event technologies (Kafka, Kinesis, Pub/Sub).
• Strong SQL skills for data modeling, transformation, and analysis.
• Proficiency in Python for data pipeline development, automation, and data processing.
• Experience working with orchestration tools such as Airflow, Dagster, Prefect, or equivalent.
• Strong understanding of ETL/ELT design patterns, data lifecycle management, and data modeling best practices.
• Familiarity with cloud environments (AWS, GCP, or Azure).
• Knowledge of data quality, observability, monitoring, and reliability concepts.
• Good communication skills and the ability to collaborate effectively with cross-functional teams.

Nice to Have:

• Experience with ClickHouse and/or other high-performance analytical databases.
• Experience with data governance or cataloging tools.
• Basic understanding of ML workflows or MLOps concepts.
• Experience with infrastructure-as-code tools (Terraform, CloudFormation).
• Familiarity with testing frameworks or data validation tools.