Data Engineer (Tableau)

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.

•       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.

•       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.

•       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.

•       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.

•       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.

•       Participate in sprint planning, stand-ups, and sprint reviews.
•       Deliver solutions on time and within scope. Adapt when priorities shift.

•       Write unit and integration tests to validate pipeline reliability and data accuracy.
•       Identify and resolve defects, performance bottlenecks, and data quality issues.

•       Stay current with cloud platforms (AWS, Azure, GCP) and emerging data engineering tools.
• Propose solutions to improve performance, security, and scalability.

Our core values of Empowering, Leadership, Innovation, Teamwork, and Excellence drive everything we do — ELITE.