Top Data Engineer Jobs in Mexico City

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.

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 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.

Hands-on Senior Data Engineer to design and maintain scalable AWS ETL/ELT pipelines, evolve a data lakehouse with Apache Iceberg, implement governance and security, optimize performance and costs, enable analytics-ready datasets, and ensure reliability, monitoring, and data quality.

The Data Engineer will design and maintain scalable ETL/ELT pipelines on Databricks and AWS, optimizing data infrastructure for gaming analytics and decision-making.

Design and build scalable data pipelines and models for research laboratory instrument outputs. Implement custom parsers, unit/integration tests, modular utilities, and dashboards (Streamlit/Tableau/Jupyter). Perform comparative dataset analysis and collaborate with product and engineering to deliver AI-enabled data solutions.

Design, build, and operationalize scalable data solutions and pipelines for customers. Analyze and transform data, ensure data quality/governance/security, test data movements, troubleshoot incidents, and collaborate with teams to support AI/ML and BI use cases.

Design, build, and scale data pipelines and analytics solutions across the full data lifecycle. Implement ETL, data modeling, and high-volume data systems using Spark/Scala, Python, Databricks, Airflow, and AWS. Collaborate with stakeholders, contribute to architecture and CI/CD practices, and deliver projects on schedule.

Build, monitor, and troubleshoot data pipelines and transformations for healthcare data. Extract, cleanse, and load data; implement data quality checks; document datasets; support API and EFT integrations; triage incidents; and participate in Agile ceremonies to estimate and deliver data solutions.

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.

Design, build, deploy, and operate scalable cloud-based data and analytics solutions. Integrate AI services (Azure OpenAI, Cognitive Services), implement monitoring, collaborate cross-functionally, and apply engineering best practices across end-to-end systems.

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.

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

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 maintain scalable cloud data pipelines and warehouses (primarily Snowflake and BigQuery). Optimize ETL/ELT, enable analytics/BI and ML use cases, implement governance/security, collaborate with distributed engineering and analytics teams, document lineage and metadata, and monitor and troubleshoot pipeline reliability.

Build and maintain integrations to retrieve, normalize, and store financial and non-financial data at scale. Develop crawlers, extractors, and a large-scale data extraction platform, reverse-engineer anti-bot solutions, work with headless browsers and proxies, design asynchronous multi-service architectures, ensure performance and reliability, and collaborate across cross-functional teams while leveraging AI tools and secure practices.