Data Engineer (Tableau)
Particle41, Argentina - Remote
The role
Data Engineer
Particle41 is seeking a talented Data Engineer to join our team. You will design, build, and maintain data pipelines and infrastructure, support client-facing data visualization, and contribute to AI-assisted data workflows. You will work across the full data lifecycle — from raw ingestion to polished, decision-ready output — in collaboration with cross-functional teams.
What you need
Requirements Gathering and Analysis
- Work with product managers and stakeholders to gather requirements and translate them into technical solutions.
- Provide technical input during requirements sessions to align data capabilities with business needs.
Skills and Experience We Value
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- 3+ years of experience as a Data Engineer.
- Strong Python proficiency.
- Experience with SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) databases.
- Hands-on experience with Tableau — dashboard development, data source management, and performance optimization.
- Familiarity with data warehousing and lakehouse principles; experience with Databricks, Spark, PySpark, and pandas.
- Experience building or supporting ML/AI data pipelines, including feature stores, vector databases, or model serving infrastructure.
- Familiarity with at least one cloud data stack (Azure, AWS, or GCP).
- Working knowledge of the ELK stack, Redis, and distributed task queues.
- Proficiency with Python libraries including Flask, scikit-learn, requests, pytest, and logging utilities.
- Comfortable working in Linux and writing shell scripts.
- Familiarity with Git and collaborative development workflows.
- Strong communication skills and the ability to work across technical and non-technical teams.
Software Development
- Design, develop, and maintain scalable ETL/ELT pipelines to process large volumes of data from diverse sources.
- Build and optimize data storage solutions — data lakes and data warehouses — for efficient retrieval and processing.
- Integrate structured and unstructured data from internal and external systems into a unified view for analysis.
- Ensure data accuracy, consistency, and completeness through validation, cleansing, and transformation.
- Maintain clear documentation for data processes, tools, and systems.