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Jobs/Data Engineer Role/Particle41 - Data Engineer (Tableau)
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Particle41

Particle41 - Data Engineer (Tableau)

Remote - Argentina2d ago
RemoteMidLATAMCloud ComputingArtificial IntelligenceData EngineerPythonTableauPerformance ManagementAzureAWSGCPELKMongoDBPostgreSQLMySQLNoSQLSQLData QualityDatabricksRedisPandasFlaskpytestLinuxscikit-learnGitSprint PlanningVectorData VisualizationShellReportingDocumentationSeabornPlotlyMatplotlib

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Requirements

• 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. • Agile Development • Agile Development • Participate in sprint planning, stand-ups, and sprint reviews. • Deliver solutions on time and within scope. Adapt when priorities shift. • Testing and Debugging • Write unit and integration tests to validate pipeline reliability and data accuracy. • Identify and resolve defects, performance bottlenecks, and data quality issues. • Continuous Learning • Continuous Learning • Stay current with cloud platforms (AWS, Azure, GCP) and emerging data engineering tools. • Propose solutions to improve performance, security, and scalability. • 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. • Tableau • 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. • Our core values of Empowering, Leadership, Innovation, Teamwork, and Excellence drive everything we do — ELITE. • E — Empowering: Enabling individuals to reach their full potential • E — Empowering: • L — Leadership: Taking initiative and guiding each other toward success • L — Leadership: • I — Innovation: Embracing creativity and new ideas to stay ahead • I — Innovation: • T — Teamwork: Collaborating with empathy to achieve common goals • T — Teamwork: • E — Excellence: Striving for the highest quality in everything we do • E — Excellence:

Responsibilities

• 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. • Data Visualization • Data Visualization • Build and maintain Tableau dashboards and reports that translate complex datasets into clear, decision-ready visuals. • Tableau • Design data models and extracts optimized for Tableau performance, including live connections and published data sources. • Partner with stakeholders to understand reporting needs and translate them into visual solutions. • Support ad hoc analysis using Tableau, Python-based charting (matplotlib, seaborn, plotly), or similar tools. • AI and Data Support • AI and Data Support • Support AI/ML workflows by building and maintaining the data pipelines that feed model training, inference, and evaluation. • Assist with data preparation for LLM and machine learning projects, including feature engineering, tokenization pipelines, and vector store integration. • Help teams adopt AI-assisted data tooling — copilots, intelligent search, automated reporting — by ensuring clean, well-structured data is available upstream. • Contribute to prompt engineering and evaluation frameworks where data context is a key input.

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