Top Data Engineer Jobs in Mexico City

Build and maintain scalable AWS data pipelines and ingestion frameworks for batch, streaming, and CDC workloads. Develop Spark and PySpark transformations, manage S3-based data lakes with Apache Iceberg, and use Glue, Athena, Lambda, and IAM. Collaborate cross-functionally, monitor pipeline health, resolve data quality issues, document architectures, and deliver reliable production data solutions.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Design, build, and optimize scalable ETL/ELT pipelines and data models using dbt, Snowflake, Informatica, and Airflow. Integrate data from multiple systems, ensure data quality and governance, support analytics and machine learning use cases, and troubleshoot pipeline performance issues. Collaborate with analysts, product managers, and business teams to deliver reliable data solutions while improving engineering processes, documentation, and platform scalability.

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.

Orion Innovation is seeking a Full Stack Engineer to support business transformation and product development through software engineering. The provided description contains no specific responsibilities, technologies, qualifications, location, travel requirements, or compensation details.

Build scalable data migration solutions for enterprise customers using Python, SQL, ETL, APIs, automation, and containerized tooling. Partner with customers, architects, project managers, and product teams to understand requirements, troubleshoot integrations, validate and reconcile data, and improve migration frameworks. Use AI-assisted development tools responsibly while maintaining code quality, security, testing, documentation, and delivery standards.

Develops data integration and analytics solutions for financial loss reporting. Partners with stakeholders, product owners, SMEs, and technology teams to document requirements, validate business rules, support source-to-target mapping and UAT, certify data products, resolve data quality issues, and improve dashboards, reports, data platforms, and downstream data consumption.

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.

Develop, test, implement, and maintain business applications and integrations between databases, enterprise systems, and external services. Create SQL queries, reports, dashboards, and data models using Power BI and Tableau. Collaborate with cross-functional teams to translate requirements into technology solutions, maintain technical documentation, support users, and implement automation and continuous improvements.

Designs and maintains automated AWS data pipelines using Python, Airflow, dbt, SQL, and cloud storage. Responsibilities include pipeline orchestration, error troubleshooting, data imports and exports, Snowflake query optimization, reusable code generation, testing, security, and GitHub-based CI/CD. The role also supports AWS services, API integrations, monitoring, data quality validation, and collaboration with global teams in English and Spanish.

Design, build, maintain, and optimize scalable ETL pipelines using PySpark and Databricks. Perform complex data transformations, improve performance and cloud cost efficiency, troubleshoot pipeline bottlenecks, enforce data quality and governance standards, and collaborate with technical and business stakeholders to deliver production-grade data solutions.

Designs and develops data engineering solutions using Azure Databricks. Requires at least five years of experience and intermediate English. The role is based in Mexico City under a hybrid schedule with three office days per week, with occasional full-week onsite requirements for planning, training, or project activities. Clients may eventually require fully onsite work.

Provides L2/L3 production support for master data management applications, including monitoring, debugging, root-cause analysis, and incident resolution. Develops Java and Python enhancements, REST APIs, SQL scripts, automated tests, and operational automation. Maintains data integrations, Airflow workflows, and batch or real-time pipelines while supporting data quality and third-party enrichment. Participates in reviews, releases, documentation, and globally distributed team collaboration.

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.

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.

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.

Designs and deploys secure AWS cloud architectures, leads enterprise migrations and application modernization, and builds Generative AI and machine learning solutions using Bedrock, SageMaker, vector databases, and RAG. Acts as the primary technical leader for client engagements, advises executive stakeholders, develops proofs of concept, drives AWS adoption and account growth, and mentors engineering teams while creating reusable infrastructure and delivery frameworks.

The Staff Engineer, Data Science role focuses on leveraging tech skills to enhance team capabilities and provide advanced solutions for client challenges.

Build and optimize cloud-agnostic Databricks Lakehouse architectures and scalable ETL/ELT pipelines using Spark and Delta Lake. Integrate MLflow for AI model operationalization, implement CI/CD and Infrastructure-as-Code deployments, tune performance and cloud costs, and enforce data governance and security. Collaborate with architects, data scientists, engineering teams, and clients to deliver production-ready AI solutions and technical handoffs.

Build and maintain data pipelines, dbt transformations, SQL models, and analytics datasets. Develop Power BI dashboards, semantic models, and DAX measures while validating data quality and troubleshooting issues end to end. Translate business needs into reporting solutions, support existing analytics environments, and independently deliver production-ready work. Collaborate with business, analytics, and engineering teams while applying AI tools to improve productivity.

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.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

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.