Data Engineer - Cloud & SaaS Integrations - Doit International

3+ years of professional experience in data engineering or a data-heavy backend role, with production ownership of pipelines that other people depend on Strong SQL - you can write, read and reason about the performance of non-trivial analytical queries Hands-on experience building and operating data pipelines with an orchestration framework. Dagster or Airflow is highly desired ; equivalent experience with Prefect, dbt or a comparable tool is relevant if you're ready to work in Dagster/Airflow 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 the realities: pagination, rate limits, partial failures, late-arriving and restated data, and vendors whose documentation is wrong A real instinct for data correctness. You are the kind of engineer who reconciles totals, questions a number that looks plausible, and builds the assertion rather than assuming AI-augmented working style - you already use AI tools across your engineering workflow and can talk concretely about what you've got out of them Experience developing solutions in the cloud and/or using cloud services Excellent communication skills in English, both written and verbal Self-organized, goal-oriented and self-motivated; confident, thorough and tenacious Experience with cloud or SaaS billing data - AWS/GCP/Azure cost and usage exports, marketplace billing, or the FOCUS specification Familiarity with Go , which we use across our backend services Experience with systems that carry financial or audit-grade correctness requirements - invoicing, metering, revenue reconciliation, or billing engines Exposure to FinOps practices or cloud financial management products BA/BS degree or equivalent practical experience

Expanding vendor coverage. This is the core of the role. Build new integrations against third-party billing and usage APIs, and get more of our customers' spend visible in the platform. Keep existing integrations current as vendors change their APIs and pricing models - which they do constantly. Drive down the marginal cost of the next integration so coverage scales faster. Working AI-augmented. Use AI daily across the full span of your work - exploring unfamiliar codebases and third-party APIs, prototyping approaches, generating and reviewing code, debugging, writing tests, and producing documentation. Push on what the tooling can do for this codebase, and bring judgement about where AI raises velocity and where a human still has to hold the quality bar. Data correctness and completeness. Own the quality of the data our integrations produce: duplication, gaps, and race conditions in ingestion and reprocessing, deduplication of spend that also arrives via cloud marketplaces, and support for customers' negotiated rates rather than public list pricing. Build the checks and reconciliation that let us prove the numbers are right rather than hope they are. Normalization across vendors. Design and build the models that make dozens of differently-shaped vendor bills comparable - consistent units, currencies, time granularity, resource and service taxonomies, and cost categories. Pipeline ownership. Own the orchestration, scheduling, and backfill mechanics for ingestion pipelines end-to-end - including making backfills a routine, safe, self-service operation. Collaborating and problem-solving. Work with product, support, and the engineers building on top of this data to understand where the gaps hurt. Propose work you think should happen; you're not here to wait for a spec.

3+ years of professional experience in data engineering or a data-heavy backend role, with production ownership of pipelines that other people depend on Strong SQL - you can write, read and reason about the performance of non-trivial analytical queries Hands-on experience building and operating data pipelines with an orchestration framework. Dagster or Airflow is highly desired ; equivalent experience with Prefect, dbt or a comparable tool is relevant if you're ready to work in Dagster/Airflow 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 the realities: pagination, rate limits, partial failures, late-arriving and restated data, and vendors whose documentation is wrong A real instinct for data correctness. You are the kind of engineer who reconciles totals, questions a number that looks plausible, and builds the assertion rather than assuming AI-augmented working style - you already use AI tools across your engineering workflow and can talk concretely about what you've got out of them Experience developing solutions in the cloud and/or using cloud services Excellent communication skills in English, both written and verbal Self-organized, goal-oriented and self-motivated; confident, thorough and tenacious Experience with cloud or SaaS billing data - AWS/GCP/Azure cost and usage exports, marketplace billing, or the FOCUS specification Familiarity with Go , which we use across our backend services Experience with systems that carry financial or audit-grade correctness requirements - invoicing, metering, revenue reconciliation, or billing engines Exposure to FinOps practices or cloud financial management products BA/BS degree or equivalent practical experience