BI Engineer (Data Analyst)
New
A
APPLYData Analytics
The preferred candidate should be based in Latin America, Hours that align to PT (Pacific Timezone) or ET (Eastern Timezone)Full-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonSQLMicrosoft Power BISnowflakeBigQuerydbt
Requirements
- Bachelor’s degree in Data Science, Computer Science, Statistics, Information Systems, or related field, or equivalent practical experience.
- 4+ years of experience in business intelligence, data analytics, or a hybrid analytics engineering role.
- Strong SQL skills across BigQuery and/or Snowflake (complex queries, CTEs, window functions).
- Hands-on experience building dashboards and reports in Looker Studio and/or Power BI.
- Working knowledge of dbt for data modeling and transformation.
- Familiarity with pipeline orchestration tools (Dagster preferred, Airflow or similar acceptable).
- Understanding of data governance and privacy compliance concepts (OneTrust or similar).
- Experience working with CRM/MarTech data sources (Customer.io, Twilio Segment, or comparable).
- Excellent communication skills for translating findings to non-technical stakeholders.
- Curious, insight-first mindset.
- Hands-on data engineering skills (Python, pipeline debugging, schema design) are a strong plus.
- Experience supporting a cloud data migration (GCP, Azure, or AWS) is a plus.
Responsibilities
- Design, build, and maintain BI dashboards and reports (Looker Studio & Power BI) for business and marketing stakeholders.
- Partner with Data Engineering to migrate and validate dashboard data models during GCP-to-Azure/Snowflake migration.
- Perform exploratory data analysis across BigQuery/Snowflake to identify trends and opportunities.
- Write and optimize SQL, and build/maintain dbt models for scalable data marts.
- Collaborate on Dagster-orchestrated data pipelines, troubleshoot issues, and contribute to data model design.
- Explore and prototype agentic AI and data science solutions (e.g., LLM-powered analysis, predictive modeling).
- Apply data governance principles, including privacy and retention deletion workflows (OneTrust) and PII handling.
- Own the end-to-end reporting lifecycle from requirements gathering to stakeholder walkthroughs.
- Identify data quality issues and partner with Data Engineering to resolve root causes.
- Stay current on BI analytics tooling and recommend platform improvements.
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