Data Engineer

We are looking for a Data Engineer to join a dynamic team focused on building scalable, secure, and high-performance data solutions. The ideal candidate will have hands‑on experience in Big Data environments, strong programming skills, and the ability to work collaboratively with business stakeholders.

Required Experience:

3+ years of hands‑on experience as a Data Engineer in professional environments. Proven track record in designing and maintaining data pipelines and data architectures. Strong practical experience with:
Databricks: working in collaborative data environments, developing notebooks, managing clusters. PySpark: building scalable data transformation workflows and processing large datasets. Power Shell: automating tasks, managing cloud resources, scripting data-related operations. Experience working with big data ecosystems and distributed computing frameworks (e.g., Spark). Demonstrated ability to collaborate with data analysts, data scientists, and business stakeholders to deliver actionable data solutions. Familiarity with data security, integrity, and scalability best practices.

Comfortable working in agile, fast‑paced environments with changing priorities. Effective communication skills in English (B2 minimum) and Spanish. Preferred Experience (Nice to Have):
Experience with Azure Data Services (e.g., Data Lake, Data Factory, Synapse Analytics). Working in high-volume data environments or enterprise‑level data infrastructures. Experience with CI/CD practices in data engineering workflows. Understanding of data governance and compliance frameworks.

• Design, build, and optimize modern data pipelines and data flow architectures to support analytics and business intelligence.
• Work with Big Data technologies, including Databricks and Apache Spark, to process and transform large‑scale datasets.
• Develop and maintain scalable data solutions using PySpark and Power Shell, ensuring automation and efficiency.
• Ensure data quality, security, and integrity across all stages of the data lifecycle.
• Collaborate with data analysts, data scientists, and business stakeholders to understand requirements and deliver reliable data products.
• Monitor and troubleshoot data pipelines and proactively identify performance improvements.
• Document data flows, technical specifications, and system designs to support long‑term maintenance and scalability.
• Stay up to date with the latest tools and best practices in data engineering and apply them to project work.
• Access to cutting‑edge technologies in a data‑driven environment.
• Collaborative and inclusive work culture, with a focus on continuous improvement.

• CI/CD for data workflows
• Data governance frameworks
• Data Ops
• Python
• SQL
• automation scripting
• distributed computing

• PAC