Top Data Engineer Jobs in Mexico City

Designs, develops, and maintains scalable batch and real-time data pipelines and curated datasets for enterprise credit risk, lending, and analytics. Builds solutions with Databricks and cloud-native platforms, implements data quality, monitoring, testing, and observability, and supports platform modernization. Partners with technical and business teams, troubleshoots production issues, contributes to architecture, improves reliability and efficiency, and mentors junior engineers.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

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.

Build and maintain ETL and data integration pipelines using Informatica/IICS, Python, MuleSoft, Snowflake, and Airflow. Responsibilities include transforming and validating data, monitoring and troubleshooting pipeline jobs, managing API connections and database access, supporting environment promotions and CI/CD deployments, resolving production issues, documenting operational processes, and collaborating with analytics, platform, infrastructure, and business teams.

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.

Own end-to-end data engineering projects and build scalable ELT pipelines using Snowflake, dbt, and Airflow. Responsibilities include performance tuning, dimensional modeling, data warehousing, BI data preparation, platform modernization, automation, risk identification, and mentoring engineers. The role also supports future AI/ML data capabilities and requires strong independent delivery in a post-acquisition SaaS data 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.

Designs, develops, and maintains scalable data pipelines and models in Databricks and Microsoft Azure. Responsibilities include data ingestion, transformation, integration, performance optimization, troubleshooting, migration, governance, documentation, and collaboration with architecture and engineering teams. The role requires translating business needs into reliable technical data solutions and supporting analytical and operational use cases.

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.

Develop SQL queries, optimize databases, and manage indexing, stored procedures, window functions, and dimensional models. Build advanced Power BI Report Server dashboards using DAX and Power Query. Own the full data lifecycle, including mastering, ETL, data modeling, KPI development, and visualization. Collaborate with business stakeholders to translate requirements into analytical solutions. AI tooling proficiency is preferred for workflow optimization and automation.

Build and maintain secure, scalable data pipelines and platform components using AWS, Snowflake, dbt, and Airflow. Modernize legacy data flows, integrate batch and streaming sources, improve testing and observability, and support self-service analytics. Develop AI-enabled capabilities such as anomaly detection and automated tagging. Partner with Finance, Risk, Product, Analytics, ML, and Engineering teams to define requirements, meet SLAs, ensure data governance, and deliver reliable infrastructure end to end.

Build production LLM agents, multi-step workflows, MCP servers, evaluation and observability systems, and document-extraction pipelines. Architect AI-ready semantic data layers over Snowflake and dbt, develop Python ingestion and Airflow workloads, and implement retrieval, embeddings, governance, security, and reliable agent operations. Lead technical design, mentor peers, and advise clients in an embedded consulting engagement.

The Senior Data Engineer will design and maintain ETL pipelines, automate report generation, and ensure data quality while collaborating with various teams.

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

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.

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.

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.

Design and optimize Databricks Lakehouse architectures and scalable ETL/ELT pipelines across AWS, Azure, and GCP. Build Spark and Delta Lake workflows for structured and unstructured data, tune performance and cloud costs, and integrate MLflow for AI model operationalization. Implement CI/CD and infrastructure-as-code deployments, enforce governance and security through Unity Catalog, and collaborate with client leaders to deliver production-ready data and AI solutions.

Senior AWS Data Engineer to design and operate AWS-based data solutions and Snowflake-centric platforms. Build data pipelines, implement Snowflake AI components, design data marts/warehouses/lakes, produce architecture drafts, and collaborate with architects and stakeholders for handoff to operations.

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.

Build and maintain data models, dashboards, reports, and dbt models supporting cross-functional decision-making. Process large datasets, establish reporting sources of truth, bridge technical and business teams, and document knowledge bases and business definitions for internal AI systems. The role also involves identifying cost-saving opportunities, communicating with stakeholders, and using AI to develop solutions and fix bugs.