Data Engineer

• Define and implement the organization’s enterprise data architecture, including data models, pipelines, and platform strategy
• Lead integrations between internal platforms such as Salesforce and CX platforms and third-party systems
• Establish and enforce standards for data quality, metadata management, lineage, and classification
• Partner with engineering, security, and compliance teams on privacy, retention, and access control policies
• Support compliance with SOC 2, HIPAA, GDPR, and PCI DSS
• Lead data risk reviews, support vendor onboarding, and ensure technical audit readiness
• Manage and mentor data engineers, analysts, and specialists
• Lead recruitment, onboarding, performance management, and professional development
• Drive adoption of data best practices and promote a data-driven culture
• Provide thought leadership on modern data stacks, cloud data architecture, and advanced analytics
• Lead selection, implementation, and ongoing management of enterprise data platforms
• Evaluate and implement ELT tools, DBT, Kafka, and cloud-native services
• Support enterprise analytics, AI/ML use cases, and enterprise-wide data warehousing initiatives
• Report directly to the Director, Enterprise Applications & Data
• Initially manage one direct report, with potential team expansion

Data Architect

• Design module content for the Fintech Engineering Specialization at Jala University
• Create labs
• Develop a reference implementation
• Build an evaluation harness
• Prepare an instructor guide

Academic Content Developer – Financial Data Engineering, Credit Systems

• Support new and existing customers with their data engineering needs
• Guide customers in making technical decisions to achieve their goals
• Work across multiple customer accounts and track and report progress
• Design, build, and operationalize complex data solutions
• Fix issues, apply transformations, and recommend data cleansing and quality solutions
• Analyze data sources and recommend their inclusion in analytical processes
• Incorporate data governance, data security, and data quality practices
• Collaborate across teams to support delivery and educate end users on data products and analytic environments
• Perform data and system analysis, assessment, and resolution for defects and incidents
• Test data movement, transformation code, and data components

Senior Data Engineer

• Design and implement scalable, efficient, and reliable data architectures
• Build and maintain data pipelines, ETL processes, and integration solutions for large volumes of structured and unstructured data
• Optimize data pipelines, databases, data warehouses, and data lakes for performance, reliability, scalability, and security
• Partner with stakeholders to understand data requirements and translate business needs into data models and solutions
• Establish and improve data quality standards, validation rules, documentation, and governance practices
• Implement monitoring and observability practices
• Provide technical leadership on architecture, scalability, reliability, monitoring, integration, and extensibility
• Lead and contribute to code reviews
• Mentor junior engineers
• Own production issues, including troubleshooting, root-cause analysis, escalation, and long-term resolution
• Independently lead multiple features and projects, coordinate work across engineers, and keep stakeholders informed
• Evolve engineering tools, platforms, and practices
• Use AI tools to improve engineering productivity, analysis, documentation, and problem-solving

• Co-develop batch and streaming pipelines, semantic models, and transformations on Databricks (Python/Spark)
• Define and maintain data contracts, quality rules, and data SLOs for each data product
• Guide domain teams on data modeling and data product boundaries
• Train, mentor and pair with domain engineers/analysts on data product ownership and good engineering practices
• Capture feedback and best practices from domains and feed improvements back into platform templates, tooling, and patterns
• Build and govern scalable machine learning solutions, ensuring reliable, reusable, and auditable outcomes
• Work closely with cross-functional teams to turn complex business data into trustworthy, well-governed AI products

Data Engineer, Databricks

• Design, build, test and operate ingestion and processing pipelines from plant OT sources into GCP
• Work with PLC event data, sensor streams, waveform captures, energy meters and image capture
• Build batch and streaming pipelines using Pub/Sub, Dataflow, Cloud Run/Functions, Cloud Composer, BigQuery and Cloud SQL/PostgreSQL
• Handle high-volume unstructured image and video data, including object storage, lifecycle and cost management, frame sampling and metadata indexing
• Own production pipelines including monitoring, alerting, cost, backfill, recovery and incident response
• Design and maintain data models, semantic layers, common data models and plant, line, station, asset and tool hierarchies
• Implement data quality controls for validation, completeness, drift, latency and lineage
• Optimize BigQuery storage, partitioning, query performance and cost
• Prepare training and inference datasets for parametric and image-based anomaly detection models
• Partner with data scientists on MLOps, reproducible datasets, versioning, monitoring and retraining triggers
• Support edge inference requirements and build datasets for value evidence and dashboards
• Apply data governance, privacy, retention and cybersecurity requirements across OT and IT boundaries
• Manage access and identity for plant, product and enterprise consumers
• Define reusable ingestion and onboarding patterns so adding a plant is configuration rather than a project
• Set data engineering standards and lead technical design for significant data platform changes
• Mentor and coach engineers and reduce single-person dependency on critical datasets and pipelines

• Diseñar, desarrollar, optimizar y mantener flujos de integración de datos utilizando Informatica PowerCenter y Databricks
• Gestionar y optimizar bases de datos Oracle, incluyendo consultas complejas, PL/SQL y ajuste de rendimiento
• Arquitectar e implementar soluciones de datos en la nube dentro del ecosistema Azure
• Proporcionar soporte técnico a ambientes de producción, resolviendo incidentes y garantizando el cumplimiento de los SLAs operativos
• Diseñar modelos de datos eficientes y pipelines de integración ETL/ELT escalables
• Colaborar con equipos multidisciplinarios para migrar, transformar e integrar fuentes de datos heterogéneas

IPC Data Engineer

• Design and operate the data foundation supporting AI systems
• Own movement, modeling, and quality of data from source systems through the warehouse into retrieval and feature layers
• Build and maintain data pipelines powering LLM pipelines, agentic workflows, and analytical products
• Model warehouse schemas and make decisions about grain, relationships, and denormalization
• Write production Python and maintain pipelines as versioned, tested, observable software
• Partner with the AI/ML Data Scientist, who owns model behavior and retrieval strategy
• Own the pipeline, schema, and data guarantees while collaborating on the algorithm, prompt, and evaluation boundary

Senior AI Data Engineer

• Design, develop, and maintain scalable data ingestion and curation pipelines from diverse sources
• Standardize, improve the quality of, and optimize data for analytical use
• Contribute to seamless end-to-end development and reliable data flow from source to insight
• Build and manage GCP data platforms using BigQuery, Dataflow, Pub/Sub, Cloud Functions, and related services
• Implement and manage data governance policies, access controls, and security best practices
• Use Astronomer and Terraform for workflow management and cloud infrastructure provisioning
• Monitor and improve pipeline and storage performance, scalability, efficiency, resource utilization, and cost-effectiveness
• Collaborate with data architects, application architects, service owners, and cross-functional teams on best practices, design patterns, and frameworks
• Automate data platform processes to improve reliability, data quality, and operational efficiency
• Communicate technical decisions to technical and non-technical stakeholders
• Translate business requirements into data asset designs and efficient code
• Document data engineering processes and promote knowledge sharing

Full Stack Data Engineer

• Design, build, and maintain scalable people-data models in the enterprise data warehouse
• Translate approved business and product requirements into technical data specifications, database designs, and reusable data assets
• Establish architecture patterns for dashboards, analytics products, scorecards, and approved AI use cases
• Partner with the Enterprise Data Team on source-data onboarding, engineering dependencies, release planning, and production implementation
• Maintain technical documentation for data models, integrations, transformation logic, dependencies, and platform components
• Identify and recommend architecture, platform, and infrastructure investments for an AI-ready People Data platform
• Establish automated data-quality monitoring, validation rules, reconciliation controls, and exception alerts
• Implement and maintain end-to-end data lineage
• Define and own production support, incident management, and escalation practices
• Monitor platform health, pipeline performance, refresh reliability, and recurring failure patterns
• Implement technical controls for access, privacy, confidentiality, retention, and sensitive-data handling
• Build and maintain the governed people-data foundation for approved AI, machine learning, and advanced analytics use cases
• Assess data readiness, reliability, representativeness, documentation, accessibility, and controls for AI use cases
• Support the People Data AI Control Tower and monitoring and traceability capabilities
• Own and execute the AI-readiness roadmap for the people data lake
• Serve as technical authority for the People Data platform infrastructure roadmap

Data Engineer jobs in Mexico

Data Architect

Academic Content Developer – Financial Data Engineering, Credit Systems

Senior Data Engineer

Senior Data Engineer

Data Engineer, Databricks

Data Engineer

IPC Data Engineer

Senior AI Data Engineer

Full Stack Data Engineer

People Data Engineer