Data Engineer

GeorgiaTEK Systems Inc.; Brasil; Presencial; BRL 180 000 - 280 000; Tempo integral

Resumo da oferta

GeorgiaTEK Systems Inc. seeks an autonomous Analytics Engineer with expert SQL and dbt skills to build reliable data products in a fast-paced environment.

The role focuses on production-grade dbt models, complex SQL transformations across large datasets, and scalable backfill strategies. The engineer will own production data pipelines, ensure data quality through tests, collaborate with Analytics, Product, Data Science and Data Engineering, and align with engineering best practices and

Qualificações

• 5+ years of experience in Data Engineering, Analytics Engineering, BI Engineering, or related data-focused role.
• Expert-level SQL skills including complex transformations, window functions, large-scale joins, dimensional modeling, query optimization, and data validation.
• Strong hands-on experience with dbt in production environments.

Responsabilidades

• Design, build, test, document, and maintain production-grade dbt models.
• Write, optimize, and maintain complex SQL transformations across high-volume datasets.
• Develop scalable incremental models, aggregations, transformations, and backfill strategies.

Conhecimentos

SQL

dbt

Stakeholder management

Data modeling

CI/CD

Git workflows

English proficiency

Ferramentas

Snowflake

Databricks

BigQuery

Redshift

Git

Descrição da oferta de emprego

A highly autonomous analytics engineer who combines expert SQL and dbt skills with strong stakeholder management capabilities and a proven track record of delivering reliable data products in a fast-paced environment.

• Design, build, test, document, and maintain production-grade dbt models.
• Write, optimize, and maintain complex SQL transformations across high-volume event, playback, subscriber, content, and partner datasets.
• Develop scalable incremental models, aggregations, transformations, and backfill strategies.
• Define and maintain clear model grain, metric definitions, lineage, dependencies, and data contracts.
• Implement dbt tests and additional validation controls to ensure data quality and reliability.
• Own production data pipelines, including monitoring, troubleshooting, validation, releases, reruns, and historical backfills.
• Coordinate and execute fixes for production incidents while minimizing business impact.
• Own workstreams from initial stakeholder request through requirements gathering, implementation, validation, release, and follow-up.
• Collaborate closely with Analytics, Product, Content Strategy, Reporting, Data Science, and Data Engineering teams.
• Participate in code reviews and contribute to engineering best practices, reusable patterns, and technical documentation.
• Maintain effective working-hour overlap with US-based stakeholders and team members.
• 5+ years of experience in Data Engineering, Analytics Engineering, Business Intelligence Engineering, or a related data-focused role.
• Expert-level SQL skills, including complex transformations, window functions, large-scale joins, dimensional modeling, query optimization, and data validation.
• Strong recent hands-on experience with dbt in a production environment.
• Demonstrated ability to design and build dbt models from scratch.
• Deep knowledge of dbt model organization, sources and references, testing and documentation, Jinja macros, incremental models, dependency management, and deployment best practices.
• Experience building efficient transformations and curated datasets on large-scale cloud data warehouses or lakehouse platforms.
• Proven experience owning production data pipelines, including incident management, validation, releases, reruns, and backfills.
• Strong understanding of data modeling principles, metric consistency, lineage, model grain, and data quality frameworks.
• Experience using Git-based workflows, pull requests, code reviews, and CI/CD development practices.
• Professional proficiency in written and spoken English.
• Availability to engage with stakeholders and participate in meetings during agreed-upon US business hours.
• Demonstrated experience directly supporting and partnering with business stakeholders, product teams, analytics teams, reporting teams, or other data consumers.
• Leading requirements gathering and scope definition discussions.
• Clarifying vague or ambiguous requests and identifying underlying business objectives.
• Translating business questions into technical requirements, data models, and implementation plans.
• Negotiating priorities, scope, timelines, and technical trade-offs.
• Managing competing requests from multiple stakeholders simultaneously.
• Communicating delays, risks, dependencies, and alternative solutions proactively and professionally.
• Experience with media, streaming, playback, subscriber, content, or platform analytics datasets.
• Experience working with modern cloud data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
• Familiarity with data observability, monitoring, and analytics engineering best practices.
• Experience supporting distributed teams and collaborating across multiple time zones.

Information about pay range or compensation package is not specified in this job description.

An equal opportunity statement or commitment to diversity and inclusivity is not specified in this job description.