Top Data Engineer Jobs in Mexico City

Designs, builds, deploys, and optimizes scalable GCP data platforms and pipelines. The role manages ingestion, transformation, governance, security, orchestration, infrastructure as code, automation, monitoring, and cost efficiency. It integrates data systems and services, translates business requirements into reliable data solutions, collaborates with technical stakeholders, documents processes, and promotes engineering best practices.

Design, develop, deploy, and operate scalable cloud-based data, analytics, automation, and tooling solutions. Integrate Azure AI and machine learning capabilities, implement monitoring and telemetry, analyze usage data, and collaborate with cross-functional teams to deliver customer-focused systems. Own solutions across design, implementation, and operations while contributing to Microsoft’s data platform innovation.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs; guide domain teams on data modeling and product boundaries; mentor engineers and analysts; and improve reusable platform templates, tooling, and engineering practices.

Build and maintain scalable data pipelines, ETL/ELT processes, data models, metric definitions, and data infrastructure. Develop production solutions with Python, SQL, Spark, dbt, Fivetran, Airflow, Databricks, Azure, and cloud storage. Collaborate with data scientists, engineers, analysts, and stakeholders to deliver reliable, accessible data and self-service tools. Troubleshoot pipeline issues, improve data quality and scalability, and use AI-assisted development tools to accelerate engineering work.

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.

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.

Build and operate the people-data platform supporting analytics and AI use cases. Design scalable data models, production pipelines, integrations, quality controls, lineage, governance, privacy, and access controls. Monitor reliability, manage incidents, document technical assets, and define infrastructure improvements. Own the people data lake’s AI-readiness roadmap and collaborate with the People Analytics Product Owner, BI Developer, enterprise data, HR technology, security, legal, and privacy teams.

Build and operate production data infrastructure, including batch and streaming pipelines, warehouses and lakehouses, retrieval systems, and data-quality controls. Design for schema evolution, reliability, cost, security, compliance, and observability using cloud platforms and modern orchestration tools. Partner directly with clients, explain technical trade-offs, and deploy containerized systems with CI/CD and infrastructure as code. Support AI applications through embedding, indexing, and retrieval pipelines.

Build and enhance data engineering frameworks, scalable data solutions, centralized ETL logic, metric definitions, and concise data models. Develop self-service data tools supporting data science, internal applications, and organizational decision-making. Collaborate with cross-functional stakeholders, use AI coding agents such as Claude Code or Codex, and write production-level Python and SQL.

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.

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.

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.

Design and implement scalable batch and streaming data pipelines using Spark and Scala on Microsoft Fabric/Synapse. Build JVM REST microservices, optimize Spark performance and data models, enforce engineering rigor (functional programming, SOLID), and maintain CI/CD pipelines with GitHub Actions, Maven/SBT. Collaborate in Agile teams to deliver reliable, high-performance data solutions.

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.

Designs and operates scalable data architectures, pipelines, ETL processes, databases, warehouses, and lakes. Improves data quality, governance, observability, security, and reliability while partnering with stakeholders to develop effective data solutions. Provides technical leadership, mentors engineers, leads code reviews and cross-functional initiatives, resolves production issues, and drives improvements to engineering platforms and practices.

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.

Design, build, operate, and scale change data capture pipelines using Debezium, Kafka or Azure Event Hubs, and Databricks. Manage streaming data across hundreds of databases and thousands of tables, ensuring reliability, low latency, schema consistency, and data quality. Troubleshoot replication lag, schema drift, backpressure, and recovery issues. Collaborate with backend and data teams on schemas and SLAs while documenting architecture and operational procedures.

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.

Leads the design, development, deployment, and maintenance of data pipelines and analytics platforms using Microsoft Fabric and Azure. Builds Lakehouse and Warehouse solutions, develops transformations with SQL, Python, and PySpark, and ensures data quality, security, performance, and governance. Collaborates with architects, developers, analysts, and stakeholders in Agile teams, contributes to CI/CD and documentation, leads technical discussions, mentors junior engineers, and provides technical guidance across client engagements.

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.

Designs, builds, and maintains large-scale data pipelines using Databricks, PySpark, and Apache Spark. Develops ETL/ELT processes, lakehouse architectures, and optimized data workflows while ensuring quality, reliability, performance, and observability. Collaborates with architects, analysts, and stakeholders to deliver scalable data solutions. Uses orchestration, version control, CI/CD, and automation practices to support modern analytics platforms.

Designs, builds, and maintains scalable data pipelines, data warehouse architecture, feature stores, model-training workflows, and real-time inference services. Develops semantic search, vector database, RAG, LLM orchestration, dashboarding, and AI-powered BI solutions. Partners with stakeholders, documents workflows, supports model deployment, and applies data engineering and MLOps best practices using Python, Spark, Hadoop, Kafka, SQL, and cloud databases.

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.