Lead Analytics Engineer
New
J
JobgetherTechnology/Education
BrazilContractLead
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSPythonSQLGCPTableauAirflowTerraformdbt
Requirements
- 8+ years of professional experience working with data in a software engineering or technology environment.
- Advanced proficiency in SQL and Python, with the ability to build and maintain production-quality analytical systems.
- Strong experience designing and managing business semantic layers, data catalog solutions, and data integrity or testing frameworks.
- Hands-on experience with dbt, including orchestration, modeling practices, and scalable analytics engineering workflows.
- Strong knowledge of relational databases and modern analytical data architectures.
- Experience with DAG-based orchestration tools such as Dagster or Airflow.
- Professional experience with Tableau or comparable business intelligence and visualization platforms.
- Experience working with cloud infrastructure, particularly AWS or GCP, and infrastructure-as-code tools such as Terraform.
- Demonstrated ability to work autonomously, proactively identify opportunities, and solve complex data-system challenges.
- Strong understanding of analytics engineering, data architecture, data quality, and business intelligence practices.
- Experience establishing engineering standards such as code review processes, design reviews, automated testing, version control, and maintainable development workflows.
- Strong communication and stakeholder-management skills.
- Demonstrated experience mentoring engineers and raising technical standards across a team.
Responsibilities
- Set the technical direction for the analytics engineering team, including architecture, engineering conventions, development practices, and implementation approaches.
- Establish and maintain a high technical standard through code reviews, design reviews, pairing, documentation, and technical knowledge-sharing.
- Own the architecture and long-term maintainability of analytical data models and the broader analytics layer.
- Define and govern the semantic layer, metric definitions, and self-service analytics capabilities so stakeholders can access consistent and trustworthy information.
- Partner closely with Product, Finance, Marketing, and Engineering to understand business needs and translate them into an actionable technical roadmap.
- Negotiate scope and priorities directly with stakeholders while balancing business requirements, technical constraints, and sustainable delivery.
- Establish a proactive analytics cadence that enables the team to identify emerging business questions and opportunities.
- Define the team's data testing strategy and establish reliable methods for validating business logic across the analytics ecosystem.
- Own operational health across the analytics layer, including monitoring, alerting, incident response, and release readiness.
- Mentor junior and experienced engineers, helping develop technical capabilities and shared understanding across the team.
- Lead large-scale data migrations and performance initiatives by defining the technical approach.
- Manage and improve large-scale data infrastructure and automation using modern software engineering principles.
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