Data Engineer (AI & Automation)

ISI Enterprises•Reston, VA

3d•$130,000 - $150,000•Remote

About The Position

IsI Enterprises is seeking a Data Engineer to join its new AI & Automation team. The role builds the data foundation for AI-driven automation across the Security Control, Managed Services, and Managed Security Services business units. Working alongside the AI Solutions Architect and AI Software Engineer, the Data Engineer owns and evolves the Snowflake data platform and supporting AWS infrastructure, designing pipelines and models that transform fragmented operational data—including ticketing, compliance, telemetry, and documentation—into trusted, secure, AI-ready datasets. Security and data governance are first-class requirements because the company serves Defense Industrial Base customers. The role covers scalable batch and streaming data pipelines, ETL/ELT processes, Snowflake data modeling and administration, AWS infrastructure deployed with Terraform, AI and automation data enablement, security controls, data quality and reliability, and collaboration with stakeholders and the engineering team. The position also supports auditable compliance needs, including CMMC and NIST SP 800-171 obligations. Candidates must provide proof of U.S. citizenship before hire and be able to obtain and maintain a U.S. Secret clearance. The role is remote and offers an annual salary range of $130,000–$150,000.

Requirements

Provide proof of U.S. citizenship before hire, as required by the employer.
Ability to obtain and maintain a U.S. Secret clearance.
At least 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, such as S3, Lambda, ECS, IAM, and VPC networking.
Hands-on experience with Terraform for infrastructure as code.
Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect and transformation tools such as 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.

Nice To Haves

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 such as pgvector, OpenSearch, or 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 such as Kafka or 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, such as SIEM, ticketing, or PSA/RMM platforms.

Responsibilities

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 across raw, curated, and consumption layers so it is consistent, documented, and reusable across business units.
Administer and optimize Snowflake, including warehouses, roles and role-based access control, masking and data protection policies, network policies, scheduled tasks, and cost monitoring.
Design, deploy, and maintain supporting AWS infrastructure using Terraform as infrastructure as code.
Integrate Snowflake and AWS logging with security monitoring tools such as Microsoft Sentinel.
Prepare data for LLM and automation use cases, including retrieval pipelines (RAG), embeddings, vector stores, and structured feature sets.
Build 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.
Embed secure data practices into pipelines, 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, such as CUI.
Support CMMC, NIST SP 800-171, and related compliance obligations through documented, auditable data flows.
Monitor production pipeline health, performance, and cost, and remediate failures quickly.
Implement data quality checks, lineage, and alerting; optimize storage and compute for performance and cost efficiency.
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 technical documentation, including data dictionaries and architecture diagrams, and explain design choices to the team.
Use AI tooling to accelerate development and delivery.