Senior Data Engineer

Overview

Build and operate Vultr's enterprise data foundation in Google BigQuery, turning operational data into reliable datasets for reporting and analytics. This is the company's first dedicated Senior Data Engineer role.

What you'll do

• Build and operate pipelines from operational systems into BigQuery, using incremental loading and change data capture where appropriate.
• Develop bronze, silver, and gold datasets, including shared business entities and reporting metrics.
• Automate data validation and source-to-target reconciliation.
• Ensure updates, deletes, and historical source corrections reach downstream datasets.
• Monitor pipeline health, handle exceptions and schema changes, and document recovery procedures and lineage.
• Partner with BI and data science teams to deliver documented, performant datasets.
• Work with Engineering and business system owners to resolve source-data gaps and define reliable data handoffs.
• Propose and implement data engineering standards for testing, deployment, security, and cost management within the agreed enterprise architecture.

What you'll need

• 5+ years of data engineering experience, including ownership of production pipelines.
• Advanced SQL and strong Python skills.
• Production BigQuery experience, including partitioning, clustering, query performance, and cost management.
• Experience with data transformation and orchestration tools such as dbt, Airflow, Cloud Composer, Dagster, or equivalents.
• Experience extracting from operational databases and handling incremental loads, updates, deletes, backfills, and historical corrections.
• Strong understanding of dimensional modeling and historical data tracking.
• Experience with automated data quality checks, reconciliation, monitoring, alerting, and recovery.
• Experience with Git, code review, automated testing, and CI/CD.
• Ability to work across Engineering, analytics, and business teams.

Nice to have

• Experience with MySQL, ClickHouse, Kafka, or Google Pub/Sub.
• Familiarity with Parquet, storage efficiency, and Spark or PySpark.
• Ability to trace data definitions and business logic through application code or an ORM; familiarity with PHP is a plus.
• Experience with Salesforce, NetSuite, Power BI, or comparable systems.
• Familiarity with IAM, service accounts, least-privilege access, and secure workload authentication.
• Experience with metadata, lineage, or data observability tools.
• Experience applying AI or automation to pipeline operations, incident investigation, or governed data access for LLM applications.
• Experience in cloud infrastructure, telecommunications, data centers, or another asset-intensive environment.

Details

• Location: Remote, United States.
• Compensation: $155,000 - $180,000, with final compensation varying by experience, background/skill set, location, and applicable laws.