Top Data Engineer Jobs in Mexico City

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.

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 the development, optimization, and scaling of production-grade data ingestion and transformation pipelines. Design bulk onboarding, backfills, migrations, and event-driven data processes with monitoring, idempotency, rollback plans, data quality controls, reconciliation, anomaly detection, and auditability. Collaborate with Platform and SRE teams to improve production safety and observability, while producing runbooks, cutover checklists, validation reports, and post-mortem follow-ups.

Designs and develops scalable data pipelines, automated ingestion processes, and data warehousing solutions. Builds dimensional and physical data models, transforms raw data using SQL and Python, and implements data quality validation and monitoring. Collaborates with analysts, data scientists, and business stakeholders to translate requirements into technical specifications. Applies data governance, access controls, security measures, and regulatory compliance practices.

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.

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.

Leads the design and development of scalable on-premises and cloud-native data platforms. Builds production-grade ETL/ELT pipelines using big data technologies, supports governance and data quality, and contributes to architecture and planning. Mentors engineers, reviews code, solves complex data issues, and promotes maintainable, testable solutions. The role requires Python, Spark, Hadoop, SQL, Databricks, orchestration tools, and DevOps practices, with Java experience preferred.

Lead the design and implementation of scalable on-premises and cloud data platforms. Build and maintain ETL/ELT pipelines, support data governance and quality, participate in architecture and planning, and mentor engineers through code reviews and technical guidance. The role requires strong Python, Spark, Hadoop, SQL, Databricks, orchestration, DevOps, and CI/CD expertise, along with effective communication in English and Spanish.

Build and maintain large-scale ETL/ELT pipelines using Databricks, Spark, Python, Hadoop, and cloud data platforms. Develop and optimize PySpark data processing jobs, improve pipeline performance and cost, and support CI/CD, testing, deployment automation, and observability. Collaborate with Product Managers to define user-focused data modules and features. Debug complex data issues and communicate effectively with technical and non-technical stakeholders.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.

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.

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.

Build and maintain end-to-end data pipelines, models, data marts, and semantic layers supporting agency, client, BI, product, and AI consumers. Integrate fragmented marketing and customer data across advertising platforms, e-commerce, analytics, and CRM systems. Manage dbt projects, multi-tenant Snowflake infrastructure, data quality, performance, and client-specific modeling. Use AI coding agents, cloud infrastructure, CI/CD, and automated testing while collaborating across product, engineering, analytics, and client teams.

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.

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.

This role involves finding audio data sources, managing cloud infrastructure for data ingestion, and collaborating on dataset roadmaps for AI models.

Lead scalable data engineering initiatives across on-premises and cloud platforms. Build production-grade ETL/ELT pipelines and distributed data solutions using Python, Spark, SQL, Databricks, and Hadoop technologies. Provide technical leadership through architecture decisions, code reviews, mentoring, testing, and engineering standards. Support data quality, governance, lineage, cataloging, access management, troubleshooting, optimization, and cross-functional planning.

Designs, develops, maintains, and troubleshoots scalable Talend ETL/ELT pipelines and production data integration solutions. Responsibilities include data transformation, SQL development, root-cause analysis, code reviews, testing, deployments, observability, and production support. The role collaborates with technical and business stakeholders and requires advanced conversational English and Spanish.

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.