Opportunities from jobgether (lever)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr. Analytics Engineer - Data Platform based in Mexico. As a Sr. Analytics Engineer - Data Platform, you will own how data is structured, defined, trusted, and delivered across the organization. You’ll design data contracts and analytical models that ensure critical metrics remain consistent across channels and teams. The role combines hands-on analytics engineering with ownership of ingestion, orchestration, quality, observability, and documentation. You’ll work closely with product, business, engineering, and data teams while setting technical standards and mentoring others. A key part of your impact will be enabling genuine self-service analytics while maintaining highly reliable data products and reporting. You’ll also help create the foundation for predictive models and machine learning capabilities to move from exploration into production.

You’ll take ownership of the analytical data foundation, balancing technical excellence, reliability, and business impact. Your responsibilities will include: Design and operate data contracts for critical metrics and datasets, partnering with domain owners and maintaining versioned definitions, tests, commits, and catalogs in dbt. Own analytical data modeling, including data layers, conventions, and dimensional models, supported by documentation and CI processes that prevent technical debt. Strengthen the data platform across ingestion, orchestration, quality, and observability using technologies such as Airbyte and Dagster. Build monitoring and quality processes that identify anomalies proactively before they affect stakeholders or reporting. Enable true self-service analytics through semantic layers and models in tools such as Omni and Hex, helping teams answer questions independently while maintaining strong data standards. Deliver high-confidence dashboards and reports for leadership, external partners, and regulatory reporting where accuracy and reliability are critical. Work closely with product and business domains while contributing to cross-functional standards for data contracts, quality, and modeling. Raise the technical bar through code reviews, pairing, mentoring, reusable practices, and engineering standards that remain sustainable across the team. Enable Data Science and Machine Learning initiatives by developing reliable datasets, reusable features, and production-ready pipelines that allow models and predictions to reach the product. Requirements: You’ll bring strong analytical engineering and data platform expertise, combined with sound engineering practices and the ability to translate ambiguous business questions into trusted data definitions. The ideal profile includes: 4+ years of professional experience in data, analytics engineering, data engineering, or BI with strong data modeling experience, including at least 2 years operating as a senior contributor or technical reference. Advanced SQL skills and strong analytical modeling expertise, including dimensional modeling, incremental loads, idempotency, and historical data management. Proven experience taking data transformations into production using dbt or equivalent technologies, with strong practices around structure, quality, testing, reusable components, documentation, CI/CD, and workflow orchestration. Experience building data pipelines, automations, and data processes with Python or equivalent technologies, applying engineering best practices such as Git, pull requests, code reviews, testing, and CI/CD. Hands-on experience with an orchestration platform such as Dagster or Airflow and managed ingestion tools such as Airbyte, Fivetran, or equivalent. Experience with PostgreSQL and/or analytical data warehouses such as Redshift, Snowflake, or BigQuery, with attention to performance and cost. Experience building semantic layers or BI models that allow other users to explore and answer questions independently using tools such as Omni, Looker, Hex, Metabase, or Power BI. Strong communication skills with business stakeholders, including the ability to turn ambiguous questions into clear, defensible metric definitions. Experience mentoring other engineers and establishing technical standards that continue to work without constant oversight. Native Spanish and professional English proficiency for documentation, collaboration, and technical tools. Additional experience that would be valuable includes: Experience with GCP or AWS and infrastructure as code using Terraform, Pulumi, CDK, or similar technologies. Familiarity with REST APIs, GraphQL, and microservices architectures. Experience working with clinical or healthcare data and related privacy and compliance requirements. Knowledge of OLAP architectures, including MOLAP and ROLAP. Experience enabling Data Science or ML workflows through training datasets, reusable features, and predictions served directly to products. Experience in a fast-growing startup environment characterized by high ambiguity, limited bureaucracy, and shifting priorities. Benefits: Access to professional growth resources, including courses, workshops, books, and other learning tools. Unlimited private medical assistance by video call for you and up to 3 family members, including general medical, nutrition, and psychological support. Access to TotalPass and tools supporting physical well-being. 9 additional personal leave days per year, in addition to statutory leave. Major Medical Expenses Insurance. Company-provided equipment required to perform the role. MXN $700 monthly support in addition to the regular salary. One-time MXN $2,500 support payment after completing the probationary period.