Tricog Health - Data Engineer - SQL/ETL (2-3 yrs)

Responsibilities :

- Design, develop, and optimize complex SQL queries to transform and extract insights from large datasets.

- Build and maintain scalable ETL pipelines using Python to ingest, process, and load data from various sources into our data lake and data warehouse.

- Implement best practices for data governance, data quality, and data security.

- Work with massively parallel processing (MPP) data warehouses like BigQuery, Redshift, and Snowflake to manage and analyze large datasets efficiently.

- Collaborate with data scientists, analysts, and other stakeholders to understand their data needs and provide robust data solutions.

- Set up and build data sources for dashboards to minimize repetitive ad-hoc data requests.

- Monitor and troubleshoot data pipelines to ensure reliability and performance.

- Document data workflows, ETL processes, and system architectures.

- Stay current with the latest data engineering technologies and methodologies to continuously improve our data infrastructure.

- Work within AWS or GCP environments; experience with multi-cloud environments is preferred.

- Develop and maintain data models using dbt (data build tool) to ensure consistency, quality, and efficiency in data transformations.

- Evaluate proof of concepts with new data solutions to improve existing architecture.

- Proven experience in writing and optimizing complex SQL queries.

- Strong proficiency in Python for ETL pipeline development.

- Experience with docker containerization and Kubernetes for pipeline orchestration.

- Experience with any MPP data warehouses such as BigQuery, Redshift, and Snowflake.

- Experience working with cloud platforms such as AWS and GCP; multi-cloud experience is a plus.

- Experience with data flow orchestration tools like Apache Airflow or Dagster.

- Experience with data modeling and transformation using dbt (data build tool).

- Solid understanding of data warehousing concepts, data modeling, and data architecture.

- Knowledge of data governance, data quality, and data security best practices.

- Excellent problem-solving skills and attention to detail.

- Strong communication and collaboration skills, with the ability to work effectively in a team environment.

- Experience with data catalogs and data lineage tools would be a plus.