Role overview

Data Engineer (Tableau)

Requirements and responsibilities

Readable role content extracted into sections for faster review.

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.
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Browse stack
FocusData EngineeringRole area
Seniority signalMiddleCandidate level
StackAWS, Azure, GCPPrimary skills
Location1 accepted countryEligibility

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