Expert Data Engineer
Nestlé S.A.2 hours ago
Mexico City, MexicoSenior
Responsibilities
- Design, build, maintain, and optimize enterprise data platform capabilities for ingestion, processing, storage, transformation, governance, and access.
- Develop reusable data pipelines, engineering frameworks, platform services, templates, automation scripts, and standard patterns.
- Architect and implement high-throughput batch, streaming, and near-real-time data processing capabilities.
- Optimize Databricks, Snowflake, Azure Synapse, and related platform components for performance, reliability, scalability, cost efficiency, observability, and resilience.
- Embed governance, security, quality, lineage, metadata, access control, auditability, and compliance into data platform capabilities.
- Build and maintain Infrastructure-as-Code, automated testing, monitoring, alerting, deployment, and environment management practices.
- Partner with data, analytics, product, architecture, security, platform, IT, and business stakeholders to deliver reusable platform capabilities.
- Troubleshoot complex data platform and pipeline issues, lead root-cause analysis, and drive durable remediation.
- Create documentation, reference implementations, runbooks, standards, and enablement materials.
- Stay current with lakehouse architectures, distributed systems, data observability, data contracts, metadata-driven automation, FinOps, MLOps enablement, and platform-as-product practices.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Management, Data Analytics, or a related field; equivalent experience will be considered.
- At least 8 years of experience in data engineering, data platform engineering, cloud data platforms, software engineering for data systems, or related technology roles.
- Expertise building and optimizing scalable distributed data systems across Databricks, Microsoft Azure, Spark, SQL-based platforms, cloud storage, and cloud-native data services.
- Advanced proficiency with SQL and a modern programming language such as Python, Scala, or Java, with strong software engineering practices.
- Deep experience with data lakes, lakehouses, data warehouses, hybrid architectures, data pipelines, orchestration frameworks, and enterprise-scale ingestion and transformation.
- Hands-on experience with batch, streaming, near-real-time processing, ETL/ELT frameworks, structured and unstructured data integration, and high-volume data movement.
- Strong knowledge of Infrastructure-as-Code, automation, monitoring, alerting, observability, incident response, root-cause analysis, and environment management.
- Strong communication, documentation, stakeholder management, cross-functional collaboration, and English-language skills.
- Preferred qualifications include Snowflake optimization and governance, medallion/lakehouse architectures, data contracts, schema evolution, change data capture, event-driven pipelines, data observability, and reliability engineering.
- Preferred experience includes Airflow, dbt, Azure Data Factory, Databricks Workflows, Informatica, Terraform, Git-based development, secrets management, platform configuration management, feature engineering, feature stores, vector search, MLOps, and GenAI data platforms.
- Azure Data Engineer, Databricks Data Engineer, Snowflake SnowPro, Microsoft Fabric Analytics Engineer, or related certifications are advantageous.
Benefits
- Full-time position with a hybrid working environment and flexible working scheme.
- Comprehensive social benefits package including a pension plan, health insurance, restaurant card, and mobility plan.
- Ongoing training, personal and professional development, and career opportunities.
- Dog-friendly campus with a medical center, canteen, and collaboration and relaxation areas.
- Recreation activities such as yoga and Zumba, plus volunteering opportunities.
Tech Stack
Categories
Data Engineering