Analytics Engineer Tech Lead
New
Based in BrazilFull-TimeLead
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLGitProduct AnalyticsData engineeringData modelingdbtGitHub
Requirements
- Proven experience as a Tech Lead or senior data/analytics engineering role, preferably in fintech or digital product environments.
- Strong expertise in data modeling, analytics architecture, and building scalable data marts and pipelines.
- Hands-on experience with dbt (mandatory) and version control tools such as Git/GitHub.
- Advanced SQL skills for large-scale data transformation and analysis.
- Strong Python proficiency for data processing, pipeline orchestration, and automation.
- Experience working with BI tools such as Metabase, Power BI, Tableau, or similar platforms.
- Ability to translate business requirements into technical data solutions and act as a bridge between product and engineering teams.
- Solid understanding of product analytics, experimentation, and data-driven decision-making.
- Bachelor’s degree or equivalent experience in Computer Science, Engineering, Data Science, Statistics, Economics, or related fields.
- Strong communication, leadership, and mentoring skills with a collaborative mindset.
Responsibilities
- Lead the architecture, design, and evolution of analytical data models focused on product metrics, customer journeys, and financial use cases.
- Define and enforce best practices for data engineering within the analytics team, including versioning, documentation, testing, and code quality standards.
- Design, build, and optimize scalable data pipelines using SQL, dbt, Python, and modern data engineering tools.
- Ensure data governance, accuracy, and reliability for dashboards, experimentation frameworks (including A/B testing), and product analytics.
- Establish and maintain centralized “source of truth” metrics to ensure consistency across the organization.
- Collaborate with product squads, engineering, and business stakeholders to define KPIs, measure product impact, and support decision-making.
- Mentor and guide analytics engineers, fostering technical growth and promoting a strong data engineering culture.
- Stay up to date with industry trends, introducing innovations, automation, and improvements to analytics engineering processes and architecture.
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