Top Data Engineer Jobs in Mexico City

Own and build batch and real-time data pipelines to support marketing and fandom initiatives. Collaborate with stakeholders to model data, deliver high-quality datasets, enable analytics and ML, source data from APIs, and partner with data scientists and analytics engineers to drive business insights.

Senior Data Engineer to design, build, and scale production data pipelines and identity/ customer graphs for AI agents. Ensure data quality, freshness, and reliability; automate data delivery, integrate third-party APIs, collaborate with cross-functional teams, evaluate code quality, and participate in on-call support.

Design, architect, and operate an agentic data engineering ecosystem: build and govern autonomous agents for ETL, synthetic data, QA, and modeling; integrate secure context via MCP servers to Snowflake, Salesforce, and AWS; validate agent outputs and translate ambiguous business requests into production data products.

Build and maintain ETL pipelines and recurring reports for content analytics, learner feedback, and business-impact cohorts. Maintain dashboards/headless analytics, run data quality checks, operate AI query tools, and support stakeholders with data validation and ad hoc requests, following established data architecture and standards.

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.

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.

The Data Architect role involves defining the data strategy, optimizing data schemas, enhancing performance, and collaborating with multiple teams for effective data management.

The Data Engineer I will design and manage data pipelines, integrate ETL processes, ensure data quality, and optimize performance while working with various cloud and on-premises technologies.

The Azure Data Engineer will participate in the software development lifecycle, including design, coding, testing, and documentation of applications, collaborating closely with team members and following best practices.

Lead AWS Data Engineer responsible for designing and implementing cloud-native data solutions, overseeing data migrations, and mentoring engineering teams.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build, and optimize scalable data pipelines and storage on Azure (data lakes/warehouses). Integrate and transform data from databases, APIs, and streaming sources; tune performance; collaborate with data scientists and stakeholders; work in Agile; mentor junior engineers and deliver robust, production-ready solutions with CI/CD practices.

Design, build, and maintain scalable ETL and data pipelines on AWS using Python and PySpark. Develop Glue/Lambda/Step Functions workflows, event-driven integrations, and data lake/warehouse solutions. Implement data validation, monitoring, and error-handling; tune SQL and performance; support APIs/JSON integrations; troubleshoot production issues; document, automate deployments, and mentor junior engineers.

Support design, build, and maintenance of ETL pipelines using Python and PySpark. Develop workflows with AWS Glue, Lambda, and Step Functions. Work with S3, Redshift, RDS, and Oracle; write complex SQL for extraction, transformation, validation, and reporting. Implement monitoring, logging, and error handling. Ingest/process API and JSON payloads. Collaborate with engineers, analysts, and stakeholders; contribute to code management, documentation, and deployment support.

The role involves understanding customer processes, validating technical designs of applications, estimating project efforts, supporting implementation activities, and mentoring colleagues in application architecture.

Design, build, and deploy scalable data lakehouse and warehouse platforms; implement and optimize ETL/ELT pipelines (Databricks, dbt); develop data models and Medallion architecture layers; manage CI/CD and Infrastructure as Code (Azure DevOps, Terraform, CloudFormation); use cloud-native serverless offerings across major clouds; ensure data security and governance; integrate pipelines with BI tools like Tableau, Looker, and Power BI; communicate technical designs to stakeholders.

Build and maintain web scraping and browser automation systems, develop production Python services, design streaming and batch data pipelines, improve data reliability and observability, automate Revenue Ops workflows, debug failures, and improve engineering quality through tests, docs, logging, metrics, traces, and AI-assisted tooling.

Design and implement AI-powered data pipelines and production workflows using LLMs, automation, and modern data engineering. Build systems that extract, clean, enrich, validate, and transform structured and unstructured data into customer-ready Excel, PDF, and Word outputs. Develop APIs and full-stack applications, optimize relational and vector databases, integrate AI orchestration frameworks, and translate prototypes into scalable enterprise solutions.

Lead architecture and implementation of scalable cloud-native data platforms and medallion-tiered pipelines. Optimize ETL/ELT, enforce data governance, security, and CI/CD/IaC. Mentor engineers, drive adoption of managed/serverless cloud services, integrate BI tools, and lead client-facing technical consulting and architectural decisions.

Designs, develops, and supports software applications, databases, reporting solutions, and automated workflows. Responsibilities include SQL development, SQL Server-to-Databricks migration, Power BI dashboard and semantic model optimization, DAX development, Power Automate maintenance, data quality analysis, production troubleshooting, SSL certificate management, stakeholder collaboration, and architecture reviews. The role also evaluates AI technologies and supports adoption across reporting, automation, and software delivery.

The role involves data collection and management for AI model training at Speechify, focusing on cloud infrastructure and collaboration with AI researchers to enhance data ingestion pipelines.

Build and maintain end-to-end AWS data pipelines: design reusable Python packages, implement ETL/Orchestration (Airflow), validate data with DBT/tests, optimize SQL for Snowflake/Redshift, integrate cloud services (S3, Lambda, EMR), implement CI/CD in GitHub, and monitor pipelines (CloudWatch, DataDog). Client-facing, hands-on individual contributor with strong troubleshooting and documentation responsibilities.

Design, build, and maintain scalable ETL/ELT pipelines and data architecture; integrate multiple data sources; ensure data quality, governance, security, and performance; monitor pipelines; support BI, analytics, and ML; document models and workflows; collaborate with stakeholders.