Top Data Engineer Jobs in Mexico

Lead the development, optimization, and scaling of production-grade data ingestion and transformation pipelines. Design bulk onboarding, backfills, migrations, and event-driven data processes with monitoring, idempotency, rollback plans, data quality controls, reconciliation, anomaly detection, and auditability. Collaborate with Platform and SRE teams to improve production safety and observability, while producing runbooks, cutover checklists, validation reports, and post-mortem follow-ups.

Designs and develops scalable data pipelines, automated ingestion processes, and data warehousing solutions. Builds dimensional and physical data models, transforms raw data using SQL and Python, and implements data quality validation and monitoring. Collaborates with analysts, data scientists, and business stakeholders to translate requirements into technical specifications. Applies data governance, access controls, security measures, and regulatory compliance practices.

Designs and operates production data platforms supporting warehouses, streaming and batch pipelines, semantic layers, RAG systems, embeddings, vector stores, and AI feature pipelines. Responsibilities include dimensional modeling, data contracts, lineage, observability, testing, governance, cost optimization, CI/CD, cloud infrastructure, and API delivery. Partners with data scientists and stakeholders, mentors engineers, and ensures reliable, secure, and maintainable data systems.

Build and maintain scalable data pipelines using Python, PySpark, Hadoop, Databricks, and Oracle. Optimize distributed data workflows, support the full application development lifecycle, administer Linux-based processes, and collaborate on Tableau reporting. The role also involves data governance, risk assessment, production support, technical planning, and large language model expertise within Citi’s enterprise data infrastructure.

Lead the replacement of the TapClicks ingestion tool with a standalone ETL pipeline in AWS. Document legacy logic, evaluate Fivetran versus custom development, ingest Amazon DSP and other digital advertising data, and design pipelines supporting client dashboards. Collaborate with infrastructure and data science teams to align data with attribution and scoring models, while also contributing to backend and application engineering.

Leads the design and development of scalable on-premises and cloud-native data platforms. Builds production-grade ETL/ELT pipelines using big data technologies, supports governance and data quality, and contributes to architecture and planning. Mentors engineers, reviews code, solves complex data issues, and promotes maintainable, testable solutions. The role requires Python, Spark, Hadoop, SQL, Databricks, orchestration tools, and DevOps practices, with Java experience preferred.

Lead the design and implementation of scalable on-premises and cloud data platforms. Build and maintain ETL/ELT pipelines, support data governance and quality, participate in architecture and planning, and mentor engineers through code reviews and technical guidance. The role requires strong Python, Spark, Hadoop, SQL, Databricks, orchestration, DevOps, and CI/CD expertise, along with effective communication in English and Spanish.

Build and maintain large-scale ETL/ELT pipelines using Databricks, Spark, Python, Hadoop, and cloud data platforms. Develop and optimize PySpark data processing jobs, improve pipeline performance and cost, and support CI/CD, testing, deployment automation, and observability. Collaborate with Product Managers to define user-focused data modules and features. Debug complex data issues and communicate effectively with technical and non-technical stakeholders.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.

Build and maintain scalable data pipelines, transformation layers, and data services supporting internal reporting, business intelligence, analytics, and machine learning. Collaborate with data scientists and analysts to deploy models and solutions, integrate third-party data, optimize Snowflake warehouses, test and review code, and deliver production-ready infrastructure.

Designs and maintains scalable batch and real-time ETL/ELT pipelines, data models, Lakehouse architectures, APIs, and streaming frameworks. Builds Azure- and Databricks-based data solutions using Spark, Delta Lake, and related services. Ensures data quality, governance, security, compliance, and platform reliability while supporting DataOps, CI/CD, monitoring, and performance optimization. Collaborates with data scientists, analysts, application teams, and stakeholders on enterprise data solutions.

Design, build, and optimize large-scale data pipelines using Databricks, PySpark, ETL/ELT, and lakehouse architectures. Ensure data quality, reliability, performance, and observability while applying Spark optimization techniques. Collaborate with architects, analysts, and stakeholders to deliver scalable data solutions. Implement CI/CD, version control, workflow orchestration, and automation using tools such as Azure Data Factory, Airflow, or Fabric. Communicate technical solutions effectively across technical and non-technical teams.

Design and build data integration pipelines connecting Toast with Snowflake or Microsoft Fabric and NetSuite. Develop medallion-architecture pipelines using REST APIs, webhooks, event streams, Azure services, and Microsoft Fabric capabilities. Ensure data quality, transformation, monitoring, reconciliation, troubleshooting, and performance. Collaborate with stakeholders to translate data requirements into scalable technical solutions and establish foundational architecture for the Toast implementation.

Design, build, deploy, and operate scalable full-stack, cloud-native software and infrastructure for clinicogenomic data products. Responsibilities include distributed systems, Kubernetes, serverless services, event-driven architecture, CI/CD, infrastructure as code, technical direction, engineering standards, AI-assisted development, cross-functional collaboration, and mentoring.

Build and operate scalable Azure-based data, analytics, automation, and tooling solutions for Microsoft's Finance Data & Experiences organization. Responsibilities include software development, infrastructure as code, Azure DevOps pipelines, observability, incident response, cost optimization, security, and production reliability. The role involves managing cloud infrastructure, automating operations, integrating AI capabilities, and collaborating with cross-functional engineering and business teams.

Design, build, and maintain scalable data pipelines and infrastructure using Databricks, SQL, and Python. Collaborate with analytics and business teams to improve data models, ensure data quality, implement integrations and governance, document technical designs, and mentor peers to support data-driven decision making.

Design, build, and maintain scalable data pipelines and platform components using Databricks, SQL, and Python. Collaborate with analytics and business teams to improve data models, ensure data quality and governance, document solutions, and mentor peers to support BI, reporting, and data-driven decision making.

Build, maintain, and optimize scalable data pipelines, reusable code components, and analytics data models using Databricks or Snowflake. Refactor legacy code, automate GitHub repositories, standardize KPIs and metrics, and ensure data quality, performance, and reliability. Collaborate with analytics and engineering teams to improve data architecture and processes while independently driving initiatives.

Build and maintain scalable batch and streaming data pipelines, develop and optimize data models in a cloud warehouse, implement ELT/ETL and orchestration (Airflow/Dagster), ensure data quality and monitoring, handle sensitive data with HIPAA-aligned practices, and evolve the platform's data architecture while partnering with analytics, product, and engineering teams.

Build and maintain end-to-end data pipelines, models, data marts, and semantic layers supporting agency, client, BI, product, and AI consumers. Integrate fragmented marketing and customer data across advertising platforms, e-commerce, analytics, and CRM systems. Manage dbt projects, multi-tenant Snowflake infrastructure, data quality, performance, and client-specific modeling. Use AI coding agents, cloud infrastructure, CI/CD, and automated testing while collaborating across product, engineering, analytics, and client teams.

Develops data-driven solutions for finance, accounting, and regulatory reporting. Automates reconciliations, builds self-service data tools, creates dashboards and metrics, analyzes business strategies, and monitors data quality. Partners with finance, business, and technology teams to translate requirements into technical solutions while supporting data governance, security, access controls, metadata, lineage, and compliance.

Lead the design and implementation of enterprise data quality and observability frameworks. Operationalize GX Core, create rule-based validation and monitoring solutions, define data lineage and governance standards, integrate quality checks into CI/CD pipelines, and develop dashboards, alerts, and reports. Investigate root causes of data issues and collaborate with engineering, QA, product, and support teams to improve data reliability across critical workflows.

Build and operate modern lakehouse platforms using Microsoft Fabric and OneLake. Design ingestion pipelines, notebooks, transformations, curated data layers, quality checks, monitoring, alerts, and documentation. Troubleshoot pipeline and performance issues across ingestion and consumption layers, ensure reliable data products, and collaborate with architects, analytics, and BI teams to support reporting and adoption.

Designs and develops scalable data pipelines, ETL/ELT processes, and integrations that synchronize enterprise data into Algolia indices. Optimizes indexing performance, data quality, and search relevance while implementing monitoring, governance, and validation. Collaborates with application, product, and business teams to define search requirements, troubleshoots synchronization and performance issues, and supports integrations with CMS, eCommerce, enterprise, and customer-facing platforms.

Designs and maintains scalable data pipelines, data models, semantic layers, data marts, and Tableau reporting solutions. Partners with stakeholders to translate reporting needs into technical solutions, while ensuring data quality, governance, performance, and reliability. Optimizes SQL queries, ETL/ELT processes, data warehousing, and Tableau performance across large-scale cloud data platforms. Collaborates with architects, analysts, and engineering teams on enterprise data solutions.