Data Engineer (Tableau)
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