Who We Are 

At Bluehost, we believe all small businesses deserve the opportunity to succeed online. Our mission is to give small businesses the confidence to think big. We're the all-in-one platform for small businesses to grow online with powerful simplicity, premium performance, smart tools and human support. 

As Bluehost scales, good decisions depend on good data. You'll build the pipelines and warehouse infrastructure that turn raw, messy data into trusted, well-modeled insight, fueling everything from product analytics to growth strategy across a fast-growing SMB SaaS platform. 

What You'll Do & How You'll Make Your Mark 

• Design, build, and maintain reliable batch and/or streaming data pipelines that move data from source systems into the warehouse without silent failures 

• Develop and optimize data warehouse structures (fact/dimension modeling, partitioning, indexing) to support analytics and reporting at scale 

• Write and tune complex SQL across large, production-scale datasets, balancing query performance against warehouse cost 

• Use AI-assisted tools (Copilot, Cursor) to speed up pipeline development, debugging, and data-quality checks 

• Write tested, version-controlled pipeline code and contribute to CI practices that catch data-quality regressions before they ship 

• Partner directly with analytics, product, and engineering teams to translate ambiguous data questions into well-modeled, reusable datasets 

• Monitor pipeline health proactively, catching data-quality or performance issues before they hit a dashboard 

• Document data models, lineage, and pipeline logic so other teams can self-serve with confidence 

Who You Are & What You'll Need to Succeed 

• 3-5 years in data engineering or a closely related backend/data role 

• Solid experience with relational databases and dimensional data-warehousing concepts 

• Real experience building and operating data pipelines (batch or streaming), not just writing one-off scripts 

• Comfortable using AI-assisted tools to speed up development, debugging, and validation 

• Strong SQL skills and a genuine grasp of data-modeling tradeoffs (normalization, partitioning, performance) 

• Working proficiency in Python (preferred) or another language commonly used for data engineering 

• Bonus: experience with cloud data warehouses (Snowflake, BigQuery, Redshift, or Clickhouse), orchestration tools such as Airflow, Dagster, or Prefect, or event-streaming tools such as Kafka. 

• Bonus: experience in cloud services such as Oracle cloud.