Top Data Engineer Jobs in Mexico

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Develop and support applications and data systems using Python, PySpark, ETL processes, data warehousing, and data modeling. Analyze requirements, design and test solutions, optimize ETL performance, support business users, implement security measures, and advise stakeholders. The role requires experience with big data technologies, databases, cloud platforms, Unix/Linux environments, and financial or investment banking data workflows.

Support migration of archived Concur data to AWS S3 using AWS Glue Catalog and Amazon Athena. Upload and organize data in S3, create Glue Catalog tables from DDL, configure Athena, develop and run SQL queries for data retrieval and validation, interpret relational schemas, join and validate datasets, and perform basic file handling, extraction, and structured data analysis.

Manage enterprise data stewardship and governance across Finance, Commercial, HR, and Marketing. Capture and certify data definitions, facilitate workshops, resolve terminology conflicts, validate inputs, and upload approved metadata to Databricks Unity Catalog. Improve Python and Excel intake processes, establish lightweight governance procedures, maintain stakeholder progress, and document workflows for team handoff.

Designs, builds, and maintains scalable data pipelines, warehouses, data models, and integration frameworks supporting BI, reporting, AI, and analytics. The role enables Power BI datasets, improves data quality and governance, supports Azure data platforms, and partners with IT, business stakeholders, and analytics teams. Responsibilities include ETL/ELT development, data architecture, monitoring, automation, semantic modeling, API integration, and preparing governed data assets for advanced analytics and AI initiatives.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design, develop, and maintain a scalable open-source data platform centered on lakehouse architecture, ETL/ELT pipelines, workflow orchestration, and analytical databases. Build and monitor batch data-processing systems, optimize infrastructure performance and reliability, and implement data quality, governance, and security practices. Collaborate with data scientists, analysts, and backend engineers to deliver reliable data solutions across cloud and microservices environments.

Design, implement, optimize and monitor data ingestion, storage, recovery and delivery systems. Provide data management, BI/reporting support and data architecture expertise across the organization.

Lead enterprise data deployments across Latin America, integrating customer data into ClickHouse or warehouse-native Snowflake and BigQuery environments. Build orchestration pipelines, canonical data models, semantic layers, and AI-assisted discovery workflows. Partner directly with customer data teams, resolve complex integration challenges, optimize performance, and establish the region’s deployment playbook. Professional English and Spanish fluency are required, with regular customer travel and potential future relocation within Latin America.

Designs and maintains scalable Azure-based data pipelines and infrastructure for batch, real-time, analytics, and reporting workloads. Builds Databricks Spark applications, Lakehouse architectures, Delta Tables, APIs, and streaming ingestion frameworks. Responsibilities include data governance, security, compliance, monitoring, troubleshooting, performance optimization, DataOps, and CI/CD automation. Collaborates with data scientists, analysts, application teams, and business stakeholders to deliver reliable data solutions.

Build and maintain AWS-based data lakehouse infrastructure, including batch and streaming ETL/ELT pipelines, high-frequency time-series ingestion, orchestration, analytical datasets, governance, security, monitoring, alerting, and data quality testing. The role requires strong Python, PySpark, advanced SQL, open table formats, AWS data services, lakehouse modeling, and infrastructure-as-code experience.

Provides technical leadership for enterprise application, platform, and data engineering initiatives. Designs, develops, tests, troubleshoots, and maintains complex applications across multiple systems, while supporting cloud, DevSecOps, data integration, analytics, and architecture solutions. Leads technical design, performs root-cause analysis, gathers stakeholder requirements, conducts code reviews, mentors engineers, and guides projects through the software development lifecycle. The role is fully telecommutable within the United States.

Designs, builds, deploys, and optimizes cloud-based data pipelines on GCP. Integrates data from databases, APIs, and streaming platforms using ETL and ELT processes; manages BigQuery and NoSQL databases; performs data modeling and query optimization; and uses Airflow and modern transformation tools. Collaborates with data scientists, analysts, clients, and business stakeholders in distributed multinational teams.

Designs and maintains scalable cloud-native ETL/ELT pipelines, data models, Lakehouse architectures, APIs, and real-time streaming solutions. Uses Azure, Databricks, Spark, Python, SQL, and distributed data technologies to support analytics and business intelligence. Ensures data quality, governance, security, performance, and reliability while collaborating with analysts, data scientists, application teams, and stakeholders. Supports DataOps, CI/CD, monitoring, troubleshooting, and automated deployment of enterprise data platforms.

Leads the design and modernization of enterprise data platforms, pipelines, and lakehouse architectures supporting credit risk, decisioning, analytics, and AI initiatives. Architects scalable ETL/ELT and streaming solutions, establishes data quality, governance, lineage, security, and observability standards, and optimizes distributed workloads. Partners with cross-functional stakeholders, drives technical roadmaps and architectural decisions, mentors engineers, supports compliance and audits, and leads complex initiatives across multiple teams.

Build and maintain scalable batch and streaming ETL/ELT pipelines using Python, SQL, PySpark, Databricks, and Delta Lake. Design medallion data models, ingest diverse sources, optimize Spark and SQL performance, implement data quality monitoring, orchestrate workflows, and apply testing, version control, and CI/CD practices. Partner with analysts, data scientists, and stakeholders to create reliable datasets, while documenting lineage and design decisions.

Migrate insurance policy data from legacy administration systems to a modern platform. Analyze, trace, reconcile, map, transform, cleanse, and validate data across databases and files. Build SQL queries and ETL processes, resolve data quality issues, and support production data loads. Collaborate with technical and non-technical stakeholders throughout the migration lifecycle while using AI coding tools, Agile methods, and secure data-handling practices.

Design and maintain secure, scalable data pipelines and modern data lakes and warehouses for insurance use cases. Integrate structured and unstructured data, automate ingestion and transformation, implement data quality, governance, lineage, and regulatory compliance, and prepare datasets for analytics and machine learning. Collaborate with data scientists, architects, and stakeholders while documenting data solutions. Apply AI-native engineering practices, including generative AI coding tools, prompt engineering, AI-assisted development, and GenAI output validation.

Develop and maintain production data transformation pipelines using dbt, SQL, and cloud data platforms. Build incremental models, macros, tests, snapshots, seeds, sources, documentation, and lineage. Apply data quality practices and establish standards for AI-assisted development using tools such as Claude Code and GitHub Copilot. Evaluate AI tooling, integrate agentic automation, and review AI-generated code for quality, security, and intellectual property compliance.

Develop and maintain production data transformation systems using dbt, SQL, and cloud data platforms. Build incremental models, macros, tests, snapshots, documentation, lineage, and data quality frameworks. Establish standards for AI-assisted development using Claude Code or GitHub Copilot, including prompt engineering, code review, security safeguards, and productivity measurement. Integrate AI into developer workflows and automation tooling while applying practical knowledge of LLM fundamentals.

Build and maintain production data transformation systems using dbt, SQL, and cloud data platforms. Develop models, incremental pipelines, macros, tests, snapshots, documentation, lineage, seeds, sources, and packages. Establish standards for AI-assisted development using Claude Code or GitHub Copilot, including prompting, code verification, security guardrails, and review practices. Integrate AI into developer workflows and automation tooling while evaluating productivity and quality outcomes.

Build and maintain production data transformations using dbt, SQL, and cloud data platforms. Develop models, incremental pipelines, macros, tests, snapshots, documentation, and lineage. Establish data quality practices and use AI-assisted development tools such as Claude Code or GitHub Copilot. Set standards for secure, reliable AI-generated code and integrate AI into developer workflows and automation.

Build and maintain production data transformation pipelines using dbt and SQL across cloud data platforms. Develop incremental models, macros, tests, snapshots, seeds, sources, documentation, and data lineage. Use AI coding tools such as Claude Code or GitHub Copilot daily, establish standards for AI-assisted development, evaluate generated code, and integrate AI into developer workflows and automation pipelines.

Build and maintain large-scale data pipelines, perform complex transformations, integrate models, and optimize Spark code. Develop extraction, transformation, and loading infrastructure using Azure, Databricks, and SQL. Collaborate with teams, document features, transition solutions to L2 support, and ensure efficient code performance and memory utilization.

Build and maintain Snowflake data models, dbt transformation layers, and cloud-based ETL/ELT pipelines. Optimize Snowflake performance and costs, develop Python automation, implement data quality checks, maintain lineage and documentation, and collaborate with analysts and reporting teams. Participate in code reviews, testing, deployments, and continuous process improvement.