Lead the data engineering team to support, maintain, and enhance PDS data products and ETL pipelines.
Responsibilities
Support, maintain, and enhance PDS data products, ETL pipelines built using DBT, AWS Glue, Databricks, and other cloud‑native technologies.
Analyze, troubleshoot, and resolve production incidents, ensuring high availability, reliability, and adherence to SLAs.
Develop pipeline enhancements, bug fixes, and operational automation to improve platform performance, scalability, and operational efficiency.
Ensure data quality, integrity, and operational stability through validation, monitoring, root cause analysis, and proactive issue resolution.
Collaborate with Analytics Engineering, Cloud Engineering, and Business teams to support feature releases, production deployments, and continuous improvements.
Build authoritative data products supporting DoW acquisition and program operations on Palantir Foundry.
Compensation
The range displayed represents the typical salary range for this role and is not a guarantee of compensation. Actual compensation will be based on factors unique to each candidate, including job-related skills, relevant experience, certifications, training, clearance level, and level of seniority. IntelliTech uses the full breadth of the salary range in making compensation decisions.
Responsibilities
Own architecture and implementation of scalable data pipelines and data products within Palantir Foundry
Integrate and transform data from multiple DoW and enterprise source systems
Build production transforms in Python, PySpark, and SQL
Design scheduled and incremental builds
Preserve raw source data with metadata capture
Requirements
Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical discipline
Lead internal technical authority overseeing data architecture projects for external partners, ensuring alignment with enterprise standards and strategic goals.
Compensation
The pay range for this position is $80.00 - $87.00/hr.
Responsibilities
Provide expert oversight for data projects delivered by external partners
Review partner-created architecture, code, and technical designs
Participate in meetings with implementation partners and internal stakeholders
Monitor progress and ensure adherence to operational principles
Identify and flag deviations, risks, or quality concerns
Looking for an experienced Senior Product Engineer to join the Risk & Control Governance team to build governance, assurance, and data foundations that ensure risk decisions are consistent, explainable, auditable, and continuously monitored.
Responsibilities
Building and maintaining data ownership, lineage, and quality monitoring systems
Designing continuous monitoring and validation systems to verify controls
Implementing end-to-end audit trails, change history tracking, and explainability tooling
Integrating with Risk API team to embed governance and assurance capabilities
Designing robust, high-throughput solutions for large volumes of data
Looking for an experienced Senior Product Engineer to join the Risk & Control Governance team. The role involves building governance, assurance, and data foundations to ensure every risk decision is consistent, explainable, auditable, and continuously monitored.
Responsibilities
Building and maintaining data ownership, lineage, and quality monitoring systems
Designing continuous monitoring and validation systems to verify controls perform as intended
Implementing trigger validation, drift detection, coverage analysis, and control health scoring
Implementing end-to-end audit trails, change history tracking, and explainability tooling
Designing robust, high-throughput solutions for large volumes of data
Lead and provide technical leadership for multiple data‑engineering teams supporting the Wealth domain, driving architecture, design, and end‑to‑end data pipeline delivery.
Compensation
$168,550 - $191,850
Responsibilities
Lead four data‑engineering teams supporting the Wealth domain
Provide hands‑on technical leadership across architecture, solution design, implementation, and engineering quality
Review technical designs and code, challenge engineering decisions constructively
Guide the development and operation of end‑to‑end data pipelines with a focus on Databricks
Establish and reinforce engineering standards for data quality, error handling, monitoring, and reliable pipeline development
Requirements
Bachelor’s or master’s degree in Computer Science, Engineering, Information Technology, or a related field of study.
Nice to have: Direct experience with Databricks and Spark
Nice to have: Experience leading globally distributed or offshore engineering teams
Nice to have: Experience establishing engineering standards across multiple teams or domains
Nice to have: Experience within financial services, retirement, wealth management, or investment‑related data environments
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