About the role

Data Engineer supporting growth in a fast-growing vertical SaaS company. Responsible for data analysis, reporting, and collaborating with cross-functional teams.

Responsibilities

Collect, analyze, and interpret large datasets to drive strategic decisions across the organization.
Build dashboards and reports using BI tools to track performance and identify trends.
Partner with cross-functional teams (product, sales, support, executives) to deliver data-driven insights that inform decision-making.
Collaborate directly with customers to understand data requirements and provide tailored analytical support.
Develop and optimize SQL queries to extract, transform, and analyze data from multiple sources.
Support the data infrastructure by working with Snowflake, Redshift, MySQL, MSSQL, and PostgreSQL environments.
Translate business needs into analytical solutions and present findings in a clear, compelling manner.

3–5 years of professional experience in data engineering, with hands-on responsibility for building and maintaining production data pipelines.
Strong proficiency in SQL and solid experience using Python (or a similar language) for data transformation, automation, and pipeline development.
Experience working with cloud data warehouses and relational databases such as Snowflake, Amazon Redshift, BigQuery, MySQL, or PostgreSQL.
Hands-on experience with ETL/ELT frameworks and orchestration tools (e.g., dbt, Airflow, Prefect, Fivetran, or similar).
Working knowledge of data modeling concepts, including fact/dimension tables, schema design, and optimization for analytics and product use cases.
Ability to troubleshoot data quality issues and improve pipeline reliability, performance, and monitoring.
Strong collaboration and communication skills, with experience working closely with analytics, product, and engineering partners.
Experience working with healthcare-related data or other regulated datasets (HIPAA familiarity a plus).