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About This Role
About the Role
IsI Enterprises is seeking a Data Engineer to join our new AI & Automation team. You will build the data foundation that powers AI-driven automation across our Security Control, Managed Services, and Managed Security Services business units. Working alongside the AI Solutions Architect and AI Software Engineer, you will own and evolve our Snowflake data platform and the AWS infrastructure around it, designing the pipelines and models that turn fragmented operational data (ticketing, compliance, telemetry, documentation) into trusted, secure, AI-ready datasets.
Because we serve Defense Industrial Base customers, you will treat security and data governance as first-class requirements, not afterthoughts.Key Responsibilities
Data Pipeline Development
Design, build, and maintain scalable batch and streaming pipelines that ingest data from internal platforms, SaaS tools, cloud environments, and customer systems. Build reliable ETL/ELT processes with automated testing, validation, and monitoring. Model data in Snowflake (raw, curated, and consumption layers) so it is consistent, documented, and reusable across business units.
Snowflake Platform & AWS Infrastructure
Administer and optimize the Snowflake platform, including warehouses, roles and role-based access control, masking and data protection policies, network policies, scheduled tasks, and cost monitoring. Design, deploy, and maintain the supporting AWS infrastructure (e.g., S3, Lambda, ECS, IAM, networking, and secrets management) using Terraform as infrastructure as code. Integrate Snowflake and AWS logging with security monitoring tools such as Microsoft Sentinel.
AI & Automation Enablement
Partner with the AI Solutions Architect and AI Software Engineer to prepare data for LLM and automation use cases, including retrieval pipelines (RAG), embeddings and vector stores, and structured feature sets. Build the integrations that let AI-driven workflows read from and write back to operational systems. Support evaluation and observability of AI workflows through logging and quality metrics.
Security-First Data Engineering
Embed secure data practices into every pipeline, including encryption in transit and at rest, least-privilege access, secrets management, data classification, and audit logging. Enforce data segregation and handling requirements for sensitive and controlled information (e.g., CUI). Support CMMC, NIST SP 800-171, and related compliance obligations through documented, auditable data flows.
Data Quality, Reliability, and Operations
Monitor pipeline health, performance, and cost in production, and remediate failures quickly. Implement data quality checks, lineage, and alerting. Optimize storage and compute for performance and cost efficiency.
Collaboration and Process
Work with business unit stakeholders to understand operational problems and translate them into data requirements. Contribute through pull requests, code reviews, and agile ceremonies. Write clear technical documentation, including data dictionaries and architecture diagrams, and be prepared to defend design choices to the team. Leverage AI tooling to accelerate development and delivery.Qualifications
- As part of your work at ISI, you will potentially have access to sensitive information from the federal government. Pursuant to applicable regulations, ISI requires that all candidates for this role present proof of U.S. citizenship before hire.
- Ability to obtain and maintain a U.S. Secret clearance
- 4+ years of experience designing, building, and operating data pipelines in production
- Strong proficiency in SQL and Python
- Experience with relational databases, specifically PostgreSQL
- Hands-on experience with Snowflake, including data modeling, RBAC, masking policies, and performance and cost optimization
- Experience designing, deploying, and operating AWS infrastructure (e.g., S3, Lambda, ECS, IAM, VPC networking)
- Hands-on experience with Terraform for infrastructure as code
- Experience with workflow orchestration (e.g., Airflow, Dagster, Prefect) and transformation tooling (e.g., dbt)
- Experience building integrations with REST APIs and third-party SaaS platforms
- Experience using AI tooling such as Claude, Codex, or an equivalent service to develop enterprise software
- Familiarity with version control (Git) and CI/CD practices
- Excellent problem-solving and analytical skills with keen attention to detail
- Ability to work independently and collaboratively in a fast-paced Agile environment
- Strong communication and interpersonal skills, with the ability to interact effectively with team members at all levels
Preferred Qualifications
- Bachelor's degree in computer science, data engineering, or a related technical field, or equivalent work or military experience
- Experience preparing data for LLM applications, including RAG pipelines, embeddings, and vector databases (e.g., pgvector, OpenSearch, Pinecone)
- Experience with AWS data services such as Glue, Athena, RDS, and Kinesis, and with AWS GovCloud
- Experience with Snowflake features such as Snowpark, Cortex, scheduled tasks, and Snowflake-to-SIEM log integration
- Experience with containerization (Docker, Kubernetes) and CI/CD for infrastructure deployments
- Experience with streaming technologies (e.g., Kafka, Kinesis)
- Familiarity with data governance, lineage, and cataloging practices
- Familiarity with security frameworks and compliance requirements such as CMMC, NIST SP 800-171, DFARS, or FedRAMP
- Experience working with data from security, IT service management, or MSP tooling (e.g., SIEM, ticketing, PSA/RMM platforms)
What We Offer
- $130,000–$150,000 annual salary range
- Remote work
- A casual, friendly, and relaxed work environment
- Professional growth encouragement and support
- Medical, Dental, and Vision insurance with FSA and HSA options
- PTO, 11 Paid Holidays, Paid Parental & Military Leave, and more
- Life & Disability Insurance
- 401K with match
Industrial Security Integrators, LLC ("IsI") is an equal opportunity employer committed to affirmative action and diversity in the workplace. It is the policy of IsI to provide Equal Employment Opportunities (EEO) to Employees and Applicants, without regard to race, color, religion, sex, age, marital status, citizenship status, national origin, sexual orientation, gender identity, veteran status or disability or any other factor protected by law and to provide advancement opportunities for minorities, women, disabled individuals, and veterans. IsI is stronger and more effective when our workforce includes highly qualified individuals with diverse backgrounds, cultures, and traditions.
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Salary Context
This $130K-$150K range is below the median for Data Engineer roles in our dataset (median: $145K across 47 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 11,520 AI roles we're tracking, Data Engineer positions make up 1% of the market. At ISI Enterprises, 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 $177,000 based on 195 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 21% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
ISI Enterprises AI Hiring
ISI Enterprises has 1 open AI role right now. They're hiring across Data Engineer. Based in Reston, VA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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