Remote Data Engineer - AI-Powered Billing & Data Quality

Forest Row; United States; Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Flexible Working Options

Health Insurance

Parental Leave

Home Office Allowance

Professional Development Stipend

Peer Recognition Program

Job summary

DoiT is hiring a Data Engineer to own the data side of our integrations across SaaS platforms, billing APIs, and usage data. You will expand visibility of customer spend, ensure data correctness, and design models to normalize diverse vendor bills for comparability.

You will work AI-augmented from day one, prototyping approaches, generating code, and ensuring data quality across ingestion and reconciliation, while collaborating with product, support, and engineering teams.

Qualifications

• 3+ years of professional experience in data engineering or a data-heavy backend role with production ownership of pipelines.
• Strong SQL—ability to write, read and reason about non-trivial analytical queries.
• Hands-on experience building and operating data pipelines with an orchestration framework.
• Strong Python, or another language you use fluently for data work.
• Experience with a cloud data warehouse or analytical store (BigQuery, ClickHouse, Snowflake, Redshift or similar).
• Experience integrating third-party REST APIs, including handling pagination, rate limits, partial failures and restated data.
• AI-augmented working style and ability to discuss concrete benefits from AI tools.
• Excellent communication skills in English.

Skills

SQL

Python

Data pipelines

REST API integration

AI tooling

Tools

BigQuery

Snowflake

Redshift

Airflow

ClickHouse

Job description

DoiT is hiring a Data Engineer to own the data side of our integrations across SaaS platforms, billing APIs, and usage data. You will expand visibility of customer spend, ensure data correctness, and design models to normalize diverse vendor bills for comparability.

You will work AI-augmented from day one, prototyping approaches, generating code, and ensuring data quality across ingestion and reconciliation, while collaborating with product, support, and engineering teams.