数据工程师(Tableau)
Data Engineer (Tableau)
Data Engineer
Particle41 正在寻找一位有才华的数据工程师加入我们的团队。您将设计、构建和维护数据管道和基础设施,支持面向客户的数据显示,并为人工智能辅助的数据流程做出贡献。您将与跨职能团队合作,贯穿整个数据生命周期——从原始数据采集到精炼的决策可用输出。
在本职位中,您将负责
软件开发
- 设计、开发和维护可扩展的 ETL/ELT 管道,以处理来自多种来源的大规模数据。
- 构建和优化数据存储解决方案——数据湖和数据仓库——以实现高效的数据检索和处理。
- 将内部和外部系统的结构化和非结构化数据整合成统一的视图以供分析。
- 通过验证、清洗和转换确保数据的准确性、一致性和完整性。
- 维护清晰的数据流程、工具和系统的文档。
数据显示
- 构建和维护 Tableau 仪表板和报告,将复杂的数据集转化为清晰的决策可视化内容。
- 设计针对 Tableau 性能优化的数据模型和数据提取,包括实时连接和已发布的数据源。
- 应用数据显示最佳实践——图表选择、布局、颜色和交互性——以生成客户可用的输出。
- 与利益相关者合作,了解报表需求并将其转化为可视化解决方案。
- 使用 Tableau、基于 Python 的图表工具(matplotlib、seaborn、plotly)或其他类似工具支持临时分析。
AI 和数据支持
- 通过构建和维护为模型训练、推理和评估提供数据的管道来支持 AI/ML 流程。
- 协助 LLM 和机器学习项目的数据显示准备,包括特征工程、分词管道和向量存储集成。
- 通过确保上游数据干净且结构良好,帮助团队采用 AI 辅助的数据工具——协作者、智能搜索、自动化报告。
- 在数据上下文是关键输入的地方,参与提示工程和评估框架的构建。
需求收集与分析
- 与产品经理和利益相关者合作,收集需求并将其转化为技术解决方案。
- 在需求会议中提供技术建议,以确保数据能力与业务需求保持一致。
敏捷开发
- 参与冲刺计划、站会和冲刺评审。
- 按时交付解决方案
查看英文原文
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.
In This Role You Will
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
- Build and maintain Tableau dashboards and reports that translate complex datasets into clear, decision-ready visuals.
- Design data models and extracts optimized for Tableau performance, including live connections and published data sources.
- Apply data visualization best practices — chart selection, layout, color, and interactivity — to produce client-ready output.
- 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
- 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.
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.
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
- Stay current with cloud platforms (AWS, Azure, GCP) and emerging data engineering tools.
- Propose solutions to improve performance, security, and scalability.
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.
About Particle41
Our core values of Empowering, Leadership, Innovation, Teamwork, and Excellence drive everything we do — ELITE.
E — Empowering: Enabling individuals to reach their full potential
L — Leadership: Taking initiative and guiding each other toward success
I — Innovation: Embracing creativity and new ideas to stay ahead
T — Teamwork: Collaborating with empathy to achieve common goals
E — Excellence: Striving for the highest quality in everything we do
Particle41 welcomes individuals from all backgrounds committed to our mission and values. We provide equal employment opportunities to all employees and applicants. Hiring and employment decisions are based on merit and qualifications — without discrimination based on race, color, religion, caste, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, local, or international laws.
For assistance during the application or interview process, contact us at
Originally posted on Himalayas