Senior Data Engineer

ICON is seeking a Senior Software/Data Engineer I to join our Data Intelligence & Systems Architecture (DISA) team. This engineer will play a foundational role in shaping, developing, and scaling ICON’s enterprise data platform within Palantir Foundry, owning the ingestion, modeling, and activation of data that powers reporting, decision-making, and intelligent automation across the company.

You will not just build pipelines. Instead, you will be architecting the Digital Twin of an industrial robotics and construction company. You will work closely with teams across Supply Chain & Manufacturing, Logistics, Engineering Support Services, Software, Field Operations and Technology R&D to centralize high-value data sources, model them into scalable assets, and enable business-critical use cases. Use cases will range from real-time reporting and analytics to AI/ML-driven operational workflows and decision-making. 

This is a highly cross-functional and technical role, ideal for someone with strong data engineering skills, deep business curiosity, and a bias toward action. This role is based at ICON’s headquarters in Austin, TX and reports to the Senior Director of Operations.

 

Responsibilities

• Data Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines in Palantir Foundry (and supplemental tools) to integrate data from enterprise systems (NetSuite, Workday, etc.) and external sources. Ensure pipelines are efficient and fault-tolerant, handling large data volumes from manufacturing and field operations (e.g. IoT sensor feeds).
• Data Modeling & Ontology: Develop and refine data models that organize enterprise data into meaningful structures. Leverage Foundry’s ontology features to define business objects (e.g. Customers, Work Orders, Inventory Items) and ensure data is linked in a way that reflects real-world relationships. Continuously improve these models for new business requirements – for instance, adding new attributes or new object types as we launch new programs.
• Data Quality & Governance: Implement data validation and quality checks in pipelines to ensure data accuracy and consistency. Monitor pipeline runs and set up alerts for anomalies or failures. Work with the team to enforce data governance policies (who owns which data, access controls, compliance needs) within Foundry and downstream systems.
• Cross-Functional Support: Collaborate with Data Analysts and business stakeholders to understand their data needs and optimize data outputs for analysis. For example, if Finance needs a specific revenue recognition report, ensure the pipeline provides the needed granularity. Provide ad-hoc data expertise to troubleshooting data issues or generating one-time analyses.
• Performance Optimization: Profile and tune data pipelines for performance. Optimize query logic, pipeline configuration, and resource usage so that data refreshes and analytics can occur in a timely manner (especially as data volumes grow with company expansion). Manage scheduling and orchestration of jobs to meet SLAs for data availability (e.g. daily financial report refresh by 8 AM).
• Integration with Systems: Work closely with Systems Architects to coordinate how data flows tie into system integrations. For example, if a new API integration is bringing in a type of data, design the pipeline to ingest that data and join it with relevant datasets for analytics. Provide input on what data transformations could happen in-flight vs. in Foundry to maximize efficiency.
• Tooling & Innovation: Contribute to developing internal tools or scripts to improve our data engineering workflow. This could include scripts for automating data deployments, custom plugins in Foundry, or using open-source frameworks (where applicable) to complement Foundry’s capabilities. Also evaluate new features in Foundry and emerging data technologies to keep our stack modern.
  
  
• Mentorship & Best Practices: As a senior member, guide junior data engineers in writing clean, maintainable pipeline code. Establish best practices for code versioning, documentation of data transformations, and general data engineering standards within the team.

Minimum Qualifications

• 7+ years of experience in data engineering or ETL development, handling large-scale datasets and complex pipelines. Strong foundation in data integration concepts and database design.
• Advanced Python skills for data processing (Pandas, etc.) and scripting, as well as solid SQL skills for querying and transforming data. Experience with PySpark or similar distributed data frameworks is a plus (Foundry uses Spark under the hood for big data).
• Experience building pipelines with enterprise data from systems like ERP, CRM, HRIS, or IoT sources. Familiar with different data formats (JSON, CSV, XML) and methods of extracting data (REST APIs, webhooks, database connectors).
• Hands-on experience with a modern data platform or pipeline orchestration tool. Experience with Palantir Foundry is a strong plus, but experience with alternatives (e.g. AWS Glue, Azure Data Factory, Databricks, Airflow, dbt, etc.) is acceptable if coupled with ability to learn Foundry quickly.
• Solid understanding of data modeling principles (relational schemas, star/snowflake schemas for analytics, normalization vs. denormalization tradeoffs). Ability to design schemas and data transformations that meet analytics requirements efficiently.
• Problem-solving and debugging skills: Able to investigate data discrepancies across systems, troubleshoot pipeline failures, and optimize slow queries.
• Attention to detail and commitment to data quality – recognizes the downstream impact of data issues and proactively puts checks in place.
• Good communication skills and ability to work with end users (analysts, managers) to clarify requirements. Comfortable explaining technical concepts to non-technical stakeholders when necessary.
• Bachelor’s degree in Computer Science, Data Engineering, or related field (or equivalent practical experience).

Preferred Skills & Experience

• Prior experience in a manufacturing or logistics environment where you dealt with IoT data, sensor streams, or other high-volume operational data. Understanding the nuances of such data (e.g. time-series data management) can be useful.
• Familiarity with cloud data architectures (AWS or Azure data services) as Foundry operates in cloud environments. Knowledge of storage services (S3, ADLS), data warehousing (Snowflake, Redshift), and how they integrate with pipeline tools.
• Experience with business intelligence tools or practices – able to appreciate how data will be used in dashboards or analytics so you can better tailor data outputs (experience with tools like Tableau, Power BI, or Foundry’s Contour/Quiver dashboards).
• Knowledge of DevOps and CI/CD for data pipelines – using version control (Git), and deploying code in a controlled, automated way.
• Master’s degree in Data Engineering or related field, or relevant certifications (e.g. Google Professional Data Engineer, AWS Data Analytics Specialty) indicating a commitment to ongoing learning in the field.