Analytics Engineer
New
D
DoiTData analytics
This role is based remotely in Mexico or Colombia as a full-time employee or in other LATAM locations as a contractor, Working hours that overlap meaningfully with US time zones.Full-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- Fluent English, both written and spoken; knowledge of any other language is an advantage.
- Experience
- 3–5 years in analytics engineering, data engineering, or BI engineering
- Required Skills
- PythonSQLApache AirflowCI/CDTerraformData modelingdbt
Requirements
- Have 3–5 years of experience in analytics engineering, data engineering, or BI engineering.
- Have a track record of delivering data models that other people depend on.
- Write, read, and optimize complex analytical SQL queries; reason about grain and joins; and design layered data models.
- Have professional experience building and maintaining dbt projects, including packages, macros, incremental strategies, snapshots, seeds, and CI/CD.
- Understand staging, intermediate, and mart patterns, deferred builds, state comparison, and slim CI.
- Apply software engineering practices including version control, code review, automated testing, and CI/CD.
- Debug data issues to their root cause and consider freshness, volume, duplicates, and late-arriving data.
- Translate business needs into data requirements and work with non-technical stakeholders, including Finance.
- Be comfortable navigating multiple repositories spanning transformation, infrastructure, orchestration, connectors, and cloud resources.
- Have a proven track record of using AI coding tools in a daily workflow.
- Work independently in a remote team and document work with clear written communication.
- Be fluent in written and spoken English.
Responsibilities
- Share ownership of the data platform's engineering backlog, routine model changes, source onboarding, CI/CD maintenance, and infrastructure operations.
- Build, test, and maintain data models and pipelines for dashboards, finance and investor reporting, and the AI analytics layer.
- Provision and configure BigQuery datasets, views, GCS buckets, and IAM permissions through Terraform.
- Manage data access and governance by extending RBAC, maintaining data catalog metadata, and updating documentation.
- Strengthen testing standards, observability, and alerting to improve platform reliability.
- Support Airflow DAG scheduling of dbt builds and troubleshoot build failures in Cloud Composer.
- Build and maintain Python-based Fivetran SDK connectors, including pagination, rate limiting, error handling, and automated tests.
- Partner with BI analysts to turn ambiguous questions into well-modeled datasets and contribute to code reviews and shared engineering standards.
- Use AI tooling to draft code and accelerate routine work.
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