This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Analytics Engineer based in Mexico.
This is a senior analytics engineering opportunity within a fast-growing, AI-native FP&A platform.
You’ll turn complex financial and operational data from multiple systems into reliable, production-ready analytical models.
Your work will help customers transform fragmented data into trusted insights that support better financial decision-making.
The role combines hands-on data engineering with architectural ownership, data quality, scalability, and continuous improvement.
You’ll work closely with Customer Success, Engineering, and other stakeholders to solve challenging data problems and deliver robust solutions.
As the team grows, you’ll help establish engineering standards, improve developer experience, and use AI to accelerate data transformation workflows.
This role is ideal for an autonomous, technically strong engineer who thrives in ambiguity and enjoys having meaningful ownership.
Accountabilities
- Own the development and evolution of client-facing data transformation pipelines, making them faster, more reliable, scalable, and maintainable as integrations and customers grow.
- Identify and implement effective ways to leverage AI to accelerate, improve, automate, and document data transformation work.
- Design and continuously improve data integrity and quality tests in partnership with Engineering and Customer Success, ensuring issues are detected before reaching customers.
- Collaborate with Customer Success to understand data and reporting requirements, troubleshoot issues, and develop robust transformations across diverse data sources.
- Build and maintain production-ready analytical models that transform raw financial and operational data into FP&A-ready datasets.
- Refactor and optimize critical models and pipelines to improve performance, scalability, reliability, and maintainability.
- Define technical patterns, conventions, and best practices that can be adopted across the wider data team.
- Maintain high standards for code quality, testing, documentation, and data integrity through strong implementation practices and thoughtful code reviews.
- Improve team processes and developer experience to enable faster, higher-quality delivery.
- Contribute to architectural decisions and proactively identify opportunities to strengthen the platform's technical foundations.
- Coach team members, support onboarding, and contribute to hiring and the continued development of the data engineering function.
Requirements
- Proven professional experience owning data transformation pipelines end to end, from source integration through production-ready analytical models.
- Experience working with financial or operational data; experience with financial datasets is a strong advantage.
- Strong SQL skills and hands-on experience with dbt or a comparable data transformation framework.
- Solid understanding of data flows from source systems through transformation layers to end-user consumption, with the ability to make sound technical decisions throughout the pipeline.
- Professional experience with Python and modern data engineering practices, including version control, testing, and deployment.
- Familiarity with BigQuery and Git, as well as CI/CD workflows for data transformations.
- Experience working with diverse business systems such as ERP, HRIS, CRM, and billing platforms.
- Familiarity with data ingestion and integration tools such as Airbyte, Fivetran, or Merge is an advantage.
- Experience with data observability and monitoring tools such as Datadog is beneficial.
- Strong commitment to code quality, data integrity, performance, scalability, and clear documentation.
- High level of autonomy and ownership, with the ability to navigate ambiguity, make decisions, and drive complex technical initiatives without constant direction.
- Excellent communication skills, with the ability to communicate clearly and persuasively with both technical and non-technical stakeholders.
- Strong collaboration skills and experience partnering with Customer Success, Engineering, and other cross-functional teams.
- A growth-oriented mindset, with enthusiasm for mentoring colleagues, improving team practices, and contributing to hiring and onboarding.
- Ability to work effectively in a remote-first environment while aligning with Americas-based working hours.
Benefits
- Competitive compensation for LATAM, with a listed range of $86,000–$116,000 annually, plus equity.
- Fully remote work from Mexico and other eligible locations across LATAM.
- Flexible working hours.
- Unlimited paid time off.
- Regular in-person company retreats.
- Company-provided MacBook Pro or Lenovo laptop.
- Opportunity to work on an AI-native FP&A platform addressing complex, real-world data challenges.
- Significant technical ownership and opportunities to influence architecture, standards, and engineering practices.
- Collaborative environment with opportunities for professional growth, mentoring, and technical leadership.
- Health insurance and retirement benefits are available to eligible US and Canadian employees.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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