Top Data Engineer Jobs in Mexico

Designs, develops, tests, and maintains scalable data pipelines and ETL/ELT solutions across cloud platforms, warehouses, and lakehouses. Profiles and validates source data, implements quality controls, monitoring, lineage, error handling, and automated testing, and optimizes SQL and stored procedures. Investigates pipeline failures, resolves data issues, documents technical processes, and collaborates with data owners, architects, security teams, and platform engineers while following secure data handling practices.

Build and govern transactional and credit behavior features for a credit scoring model using Databricks and Unity Catalog. Design reusable, auditable features with complete metadata, lineage, ownership, lifecycle, and temporal standards. Implement deployment pipelines, monitor jobs, apply data quality and FinOps practices, and maintain consistency between model training and production. Collaborate with modeling and platform teams while following advanced SQL, Python, and data governance practices.

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 for data products. Advise domain teams on data modeling and product boundaries, mentor engineers and analysts, and improve platform templates, tooling, and engineering practices. The role supports high-impact data engineering and machine learning projects for a multinational beverage company.

Own credit bureau furnishing operations, including data preparation, validation, submissions, reconciliation, and exception resolution. Build and debug SQL, Python, and data pipelines; investigate discrepancies; correct affected accounts; and implement testing, monitoring, and alerts. Partner with Customer Service, Compliance, and Engineering to resolve disputes and production defects. Document reporting logic, lineage, procedures, and incident resolutions while ensuring accurate, timely, and compliant credit reporting.

Design and maintain secure, scalable data pipelines and modern data lakes and warehouses for insurance use cases. Integrate structured and unstructured data, automate ingestion and transformation, implement data quality, governance, lineage, and regulatory compliance, and prepare datasets for analytics and machine learning. Collaborate with data scientists, architects, and stakeholders while documenting data solutions. Apply AI-native engineering practices, including generative AI coding tools, prompt engineering, AI-assisted development, and GenAI output validation.

Lead Databricks platform governance and engineering, including Unity Catalog, RBAC, workspace standards, data quality, lineage, cost optimization, automation, observability, and Infrastructure as Code. Develop operational dashboards, runbooks, documentation, and support processes while partnering with security, infrastructure, compliance, and data engineering teams. Provide training and knowledge transfer to platform administrators and engineers, and recommend strategies for improving platform scalability, reliability, and organizational maturity.

Lead Databricks platform governance and engineering, including Unity Catalog, RBAC, environment separation, data quality, lineage, automation, cost optimization, observability, and operational standards. Develop infrastructure-as-code solutions, dashboards, runbooks, and support procedures; partner with security, compliance, infrastructure, and data engineering teams; and provide training and knowledge transfer to establish scalable, reliable platform operations.

Build and maintain AWS-based data lakehouse infrastructure, including batch and real-time ETL/ELT pipelines for high-volume time-series data. Optimize storage, performance, cost, and data consistency; manage orchestration workflows; deliver analytics-ready datasets; and implement governance, security, monitoring, alerting, and data quality controls. The role also involves working directly with data users and supporting lakehouse modeling, incremental loads, deduplication, and historical backfills.

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.

Build and maintain scalable data pipelines using Python and Airflow; transform, model, and validate data for downstream analytics users. Implement monitoring, logging, alerting, testing, and quality controls. Optimize cloud data systems for performance and cost, contribute to architecture and infrastructure-as-code decisions, and document processes. Collaborate with analytics engineers, analysts, and data scientists while supporting reliable enterprise data warehouses.

Designs and maintains scalable AWS data pipelines supporting batch, streaming, and CDC processing. Builds Spark and PySpark workflows, manages S3-based data lakes with Apache Iceberg, and uses Glue, Athena, Lambda, and IAM for orchestration, querying, event processing, and access control. The role monitors pipeline health, resolves data quality issues, documents architecture, and collaborates with analysts, data scientists, and platform engineers. Requires at least two years of hands-on AWS data engineering experience.

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.

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.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

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.

Design and maintain scalable Snowflake data models, dbt projects, and ELT/ETL pipelines. Optimize Snowflake performance and costs, develop Python-based workflows, and integrate data from ERP, MES, IoT, APIs, and files. Implement data quality, governance, lineage, testing, documentation, and CI/CD practices. Support analytics and reporting through optimized datasets and BI tools such as Power BI and Tableau.

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.

Develop and maintain Power BI dashboards, reports, semantic models, and reusable datasets. Partner with business and technical stakeholders to define KPIs, data requirements, and visualization solutions. Integrate and troubleshoot data from Snowflake, SAP HANA, and enterprise source systems across the full data flow. Improve data quality, governance, consistency, and reporting reliability while documenting logic and supporting Agile delivery with distributed teams.

Architects and builds secure, scalable data platforms and governance automation. Develops metadata, lineage, data quality, access control, policy enforcement, APIs, MCP tools, and enterprise integrations. Implements identity, authorization, credential management, testing, CI/CD, monitoring, observability, and production support across environments. Partners with governance and engineering teams, documents solutions, and mentors engineers while guiding technical direction.

Develops Power BI dashboards, reports, semantic models, and data solutions using enterprise data from Snowflake and SAP HANA. Partners with business and technical stakeholders to define KPIs, gather requirements, integrate data, troubleshoot reporting pipelines, and improve data quality and consistency. Documents reporting logic and data definitions while promoting reusable analytics solutions in an Agile, distributed-team environment.

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.

Lead the architecture and development of scalable, low-latency data infrastructure for real-time insights, predictive systems, agentic AI, and generative AI applications. Build streaming ingestion, retrieval, vector, and memory systems; establish engineering, quality, and CI/CD standards; mentor data engineers; and collaborate with machine learning, product, creative, and client teams. The role requires strong cloud, Python, SQL, real-time pipeline, and AI backend expertise.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Mastercard seeks a Site Reliability Engineer focused on data protection to support and improve the Commvault backup environment, storage platforms (VAST, NetApp, Oracle ZFS, Data Domain), monitoring/observability (Grafana, Prometheus), and automation (Chef, Ansible, Jenkins, Bitbucket, Python/Bash). Responsibilities include troubleshooting Unix/Linux/Windows, designing automation/monitoring solutions, supporting disaster recovery, documenting designs/processes, and collaborating across global teams to enhance reliability and operational excellence.

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.