We are looking for a talented 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.
Youll collaborate closely with Senior Engineers, Analysts, Data Scientists, and stakeholders across the business to deliver reliable, well-structured, and well-governed data solutions.
Youll collaborate closely with Senior Engineers, Analysts, Data Scientists, and stakeholders across the business to deliver reliable, well-structured, and well-governed data solutions.
Requirements:
2-5+ years of experience as a Data Engineer or similar role.
Hands-on experience with Snowflake (mandatory)-including SQL, modeling, and basic optimization.
Experience with dbt (or similar)-model development, tests, documentation, and version control workflows.
Strong SQL skills for data modeling and analysis.
Proficiency with Python for pipeline development and automation.
Experience working with orchestration tools (Airflow, Dagster, Prefect, or equivalent).
Understanding of ETL/ELT design patterns, data lifecycle, and data modeling best practices.
Familiarity with cloud environments (AWS, GCP, or Azure).
Knowledge of data quality, observability, or monitoring concepts.
Good communication skills and the ability to collaborate with cross-functional teams.
Nice to Have:
Exposure to streaming/event technologies (Kafka, Kinesis, Pub/Sub).
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.
2-5+ years of experience as a Data Engineer or similar role.
Hands-on experience with Snowflake (mandatory)-including SQL, modeling, and basic optimization.
Experience with dbt (or similar)-model development, tests, documentation, and version control workflows.
Strong SQL skills for data modeling and analysis.
Proficiency with Python for pipeline development and automation.
Experience working with orchestration tools (Airflow, Dagster, Prefect, or equivalent).
Understanding of ETL/ELT design patterns, data lifecycle, and data modeling best practices.
Familiarity with cloud environments (AWS, GCP, or Azure).
Knowledge of data quality, observability, or monitoring concepts.
Good communication skills and the ability to collaborate with cross-functional teams.
Nice to Have:
Exposure to streaming/event technologies (Kafka, Kinesis, Pub/Sub).
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.
This position is open to all candidates.
