Top Data Engineer Jobs in Leon

The Senior Databricks Engineer will design, build, and maintain scalable data pipelines, optimize ETL/ELT processes, and ensure data quality and governance while collaborating with teams to meet data requirements.

Responsible for managing data acquisition, curation, and publishing for analytics, developing data products, and creating scalable data pipelines.

Design, build, and optimize scalable data solutions enabling integration, processing, and availability of data for analytics, BI, and data science. Collaborate with multidisciplinary teams to implement ETL/ELT, data quality, testing, cloud deployments, APIs, and DevOps/DataOps practices across Azure and GCP.

As a Data Engineer, you will enhance the US Gas product by building data scrapers, improving APIs, and collaborating on features with the product team.

Design and maintain end-to-end data pipelines using Microsoft Fabric and Azure Synapse, ensuring data quality and performance optimization while collaborating with cross-functional teams.

Design and maintain data pipelines, optimize data processing with PySpark on Databricks, ensure data quality, and collaborate on data architecture solutions.

Drive quality assurance strategy for data migration projects, validating data integrity and automation using AWS technologies and Python scripts.

The role involves designing and maintaining data pipelines, developing ETL/ELT processes, building Power BI dashboards, and ensuring data quality and governance.

Design, build, and maintain ETL pipelines and orchestration on AWS using Python/PySpark. Integrate APIs and event-driven systems, optimize storage/querying (Redshift/RDS/Oracle/S3), implement data quality and monitoring, support CI/CD deployments, and collaborate with stakeholders to deliver data solutions.

Lead the migration of SQL Server and PostgreSQL to Azure SQL. Design and support PaaS and DBaaS offerings while optimizing database instances.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Lead design, build, and optimize scalable data architectures and end-to-end ETL pipelines. Ensure data quality, governance, performance monitoring, mentor engineers, and collaborate with cross-functional teams to deliver reliable data platforms.

Support design, build, and maintain AWS-based ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions), S3/Redshift/RDS/Oracle, write SQL, implement monitoring/logging, ingest API/JSON data, and assist deployment, documentation, and code management.

Design, build, and maintain ETL pipelines and near-real-time data systems on AWS using Python and PySpark. Orchestrate workflows, implement event-driven integrations, optimize storage/querying (Redshift, RDS, S3), enforce data quality and monitoring, participate in CI/CD, and collaborate with stakeholders to deliver data solutions.

Design and build scalable data pipelines and distributed processing systems on Databricks. Lead technical design, develop advanced data models, champion CI/CD and IaC, ensure operational reliability, mentor engineers, and collaborate cross-functionally to support analytics and ML use cases.

Design, build, and operationalize scalable data solutions for customers; support data ingestion, transformation, quality, governance, and analytics to enable AI/ML and BI. Troubleshoot, test pipelines, collaborate with teams, and guide customers on technical decisions.

The Senior Data Engineer develops and manages data management solutions on AWS, focusing on building cloud-native data platforms, ETL jobs, and optimizing database performance.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

Design, build, and operate Alpaca's scalable data platform and lakehouse infrastructure. Build low-latency streaming and CDC ingestion, batch pipelines landing in Iceberg, and tooling for BI and AI consumers. Manage IaC, Kubernetes deployments, monitoring/incident response, and partner with DevOps and analytics teams to meet growing data requirements.

Design and build self-service analytics portals and responsive dashboards. Integrate data pipelines from Databricks, S3, Salesforce and internal APIs. Implement secure auth and role-based access. Collaborate with product and data teams, remain hands-on, and mentor junior developers to deliver scalable, customer-facing analytics experiences.

The Senior Software Engineer will design and operate data ingestion and processing systems for multimodal data, ensuring quality, security, and compliance while optimizing for performance and reliability. Responsibilities include building pipelines, handling messy data, and partnering with teams to enhance the platform's capabilities.

Lead design and delivery of end-to-end healthcare data platforms using Snowflake, dbt, and Azure services. Model EHR/claims/lab data, build and optimize pipelines (Python/SQL), ensure regulatory/privacy/security compliance, apply data quality frameworks, run stakeholder workshops, and leverage AI tools to accelerate development and governance.

Build and maintain secure, scalable data infrastructure and pipelines using modern data tools. Implement orchestration (Airflow), transformations (DBT), and CI/CD/Infrastructure as Code. Collaborate with cross-functional teams, improve platform reliability, and document best practices to deliver high-quality data for analysis and reporting.

Build secure, scalable data infrastructure and pipelines; design and implement data orchestration and transformation solutions with Airflow/DBT/Snowflake; ensure high-quality data delivery for analysis and reporting; automate deployments with IaC; improve observability; document best practices; support DataOps initiatives. Hybrid role in Mexico City (twice weekly onsite).