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About This Role
About us
We are professional, agile and our goal is to provide consulting services that exceed our customers expectations.
Position Summary
We are seeking two experienced Data Scientists / Palantir Data Engineers to support the acceleration of Navy ERP\+ SAP data migration through the development of advanced analytics and data engineering solutions within the Palantir Foundry platform. In this role, you will build scalable data quality and migration capabilities that improve the accuracy, transparency, and efficiency of enterprise SAP migration efforts.
This role is ideal for professionals who enjoy solving complex enterprise data challenges and leveraging Palantir Foundry to deliver high\-impact analytics, data quality improvements, and mission\-critical SAP migration capabilities.
Required Security Clearance
- Active U.S. Secret Security Clearance Ability to maintain clearance throughout employment U.S. Citizenship required. Dual citizenship not permitted
Key Responsibilities
- Design, develop, and maintain data engineering pipelines and analytics solutions using Palantir Foundry. Build large\-scale data profiling capabilities to assess SAP ERP data quality and readiness for migration. Develop automated anomaly detection models to identify data inconsistencies, duplicates, and integrity issues. Create data cleansing, matching, and standardization workflows to improve migration quality. Design reconciliation controls to validate migrated data and ensure completeness and accuracy. Develop migration dashboards, KPIs, and reporting solutions that provide stakeholders with real\-time migration metrics. Collaborate with functional SAP, ERP, and business teams to define migration requirements and data quality rules. Optimize data pipelines for performance, scalability, and reliability. Support data governance, metadata management, and lineage within Palantir Foundry. Assist with testing, validation, and deployment of migration analytics solutions. Document technical designs, data models, and operational procedures.
Required Qualifications
- Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, or a related technical field. 3\+ years of experience in data engineering, data science, analytics, or enterprise data management. Hands\-on experience with Palantir Foundry. Experience working with SAP ERP data, particularly in migration or modernization initiatives. Strong SQL skills and experience developing scalable ETL/ELT pipelines. Proficiency in Python, Spark, or similar data engineering technologies.
Experience performing:
- Data profiling Data quality assessment Data cleansing Record matching and deduplication Data reconciliation Knowledge of enterprise data modeling and metadata management. Experience developing dashboards and data visualizations. Strong analytical, troubleshooting, and problem\-solving skills. Excellent written and verbal communication skills.
Preferred Qualifications
- Experience supporting Department of Defense (DoD) or U.S. Navy programs. Familiarity with Navy ERP or large federal ERP modernization efforts. Experience with SAP S/4HANA migration projects. Knowledge of Master Data Management (MDM) concepts. Experience with cloud\-based data platforms (AWS, Azure, or Google Cloud). Familiarity with Agile software development methodologies. Active Secret or higher security clearance (or ability to obtain one).
Technical Skills
- Palantir Foundry SAP ERP / SAP S/4HANA SQL Python Apache Spark ETL/ELT Development Data Quality Frameworks Data Profiling Data Governance Data Reconciliation Data Matching \& Deduplication Dashboard Development Git REST APIs Cloud Data Platforms (preferred)
What You'll Deliver
- Scalable Palantir Foundry data engineering solutions Automated data profiling and anomaly detection capabilities Data cleansing and matching workflows Migration reconciliation controls Executive dashboards and migration metrics High\-quality, trusted data that accelerates Navy ERP\+ SAP migration success
Job Type: Full\-time
Pay: $120,000\.00 \- $160,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Flexible schedule
- Health insurance
- Paid time off
- Retirement plan
- Vision insurance
Application Question(s):
- Do you have a US Citizenship without having a dual citizenship elsewhere?
Education:
- Bachelor's (Preferred)
Work Location: Remote
Salary Context
This $120K-$160K range is below the median for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At ITS Consulting, Inc., this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($140K) sits 24% below the category median. Disclosed range: $120K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ITS Consulting, Inc. AI Hiring
ITS Consulting, Inc. has 1 open AI role right now. They're hiring across Data Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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