What are the responsibilities and job description for the Data Engineer - Palantir Foundry position at Reqroute, Inc?
Position : Data Engineer - Palantir Foundry
Location : Dallas TX 75252 -2-3 days onsite in Dallas Texas
Type : FULLTIME ROLE (NO C2C OR W2)
Hybrid
Relocation expenses will be paid by the client.
Skills: Data Engineer, Python, SQL, ETL, Palantir Foundry, Pyspark, Pandas
Job Requirements:-
- Bachelor’s degree in information technology, computer science, or a related field (or equivalent experience)
- 5 years of progressive experience in data analytics, analytics engineering, or business intelligence roles
- Must have intermediate experience with Microsoft Office products to include Word, Excel, PowerPoint, and Outlook
- Must have strong hands-on experience with Palantir Foundry, including Pipeline Builder and Workshop
- Must have advanced proficiency in PySpark and SQL for large-scale data transformation
- Must have strong Python skills (pandas, familiarity with polars is a plus)
- Must have a solid understanding of data modeling and medallion architecture
- Must have experience integrating data from ERP, CRM, POS, or similar enterprise systems
- Must have experience implementing or supporting data governance and access control models
- Must have proficiency with Git-based version control and collaborative development workflows
- Must have the ability to operate independently, manage ambiguity, and own outcomes
- Detail oriented with excellent time management and follow up skills
- Must have strong communication skills with experience engaging both technical peers and business stakeholders
- Experience integrating with Meta Ads, Google Ads, or other marketing platforms preferred
- Palantir certifications preferred
- Industry experience in maritime, real estate, hospitality, or asset-heavy, multi-unit operating environments preferred
- Experience supporting analytics in finance, operations, or revenue management contexts preferred
Job Responsibilities
- Data Engineering & Platform Ownership
- Design, build, and own end-to-end ETL/ELT pipelines across diverse data sources using Palantir Foundry, PySpark, pandas, and SQL
- Architect scalable, resilient data integration solutions supporting analytics, reporting, and operational use cases
- Own ingestion, transformation, validation, and monitoring workflows to ensure data accuracy, availability, and performance
- Manage and optimize data lake and warehouse environments within a cloud-based SaaS ecosystem
- Establish and maintain integrations with enterprise systems and external platforms (e.g., ERP, CRM, advertising platforms)
- Troubleshoot complex data issues, perform root-cause analysis, and implement long-term fixes
- Analytics Enablement and Data Applications
- Develop analytics-ready datasets and curated data products that enable self-service reporting and analysis
- Build and maintain custom analytics applications using Palantir Foundry Workshop and backend tools (Pipeline Builder, PySpark, pandas)
- Partner with business analysts, analytics consumers, and department leaders to translate commercial and operational questions into scalable data solutions
- Automate reporting and dashboarding workflows to reduce manual effort and improve data accessibility
- Enable advanced analytical use cases by delivering feature-ready datasets (e.g., for forecasting, trend analysis, or anomaly direction), without owning full data science model lifecycle unless explicitly required
- Data Governance, Standards, and Security
- Support and enhance data governance frameworks that improve data quality, consistency, security, and compliance
- Collaborate with stakeholders to define and enforce data standards, naming conventions, and access policies
- Enhance and maintain role-based access controls (RBAC) in partnership with IT and security teams
- Contribute to medallion architecture design, data modeling standards, and metadata and/or documentation practices
- Technical Leadership and Mentorship
- Serve as a senior technical resource for the team, providing design guidance, code reviews, and architectural input
- Mentor junior and mid-level engineers through structure feedback, documentation standards, and modeling best practices
- Set and reinforce expectations for maintainability, reliability, and clarity in data engineering work
- Participate in design reviews and help establish long-term technical direction for the data platform