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# Senior Data Engineer

- **Company:** Intellitech
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Big Data, Cloud Database, Cloud Storage, Information Systems, Computer Programming, Continuous Integration, Data Cleansing, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Systems, Relational Databases, Database Development, Python (Programming Language), Meta-Data Management, NoSQL, Performance Tuning, Query Optimization, Cloud Services, DataOps, Software Engineering, SQL Databases, Data Streaming, Enterprise Data Management, Feature Engineering, Apache Spark, Containerization, Pyspark, Kubernetes, Information Technology, Enterprise Integration, Apache Kafka, Non-relational Database, Software Version Control, Data Pipelines, Devsecops, Docker, Databricks
- **Published:** November 5, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9289513/senior-data-engineer

## About the Role

The ideal candidate brings strong hands-on experience with Databricks, PySpark, Python, SQL, and cloud-based data engineering, along with a proven ability to work across large data environments, integrate disparate data sources, and deliver reliable, secure, and high-performing data solutions., Active DoD Secret Clearance
Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or a related technical discipline and 8+ years of relevant experience; or Master's degree in a related field and 6+ years of relevant experience.
Strong hands-on experience designing, building, and maintaining production-grade data pipelines and integration solutions.
Strong experience with Databricks, Apache Spark, and PySpark in enterprise data environments.
Strong programming skills in Python and SQL; experience with Java or similar languages is a plus.
Experience implementing ETL/ELT pipelines and data integration processes across multiple sources and platforms.
Experience with relational and non-relational databases, data modeling, and query optimization.
Experience working in cloud-based data environments such as AWS, Azure, or GCP.
Experience with source control, CI/CD pipelines, and modern software engineering practices.
Ability to identify and resolve issues related to data quality, pipeline performance, and operational reliability.
Strong communication and collaboration skills, with the ability to work effectively across technical and non-technical teams., Experience supporting enterprise data platforms in DoD or other federal environments.
Experience with data governance, metadata management, lineage, and access control concepts.
Experience supporting AI/ML data preparation and feature engineering workflows.
Experience with Airflow, Kafka, Docker, Kubernetes, or related orchestration and containerization technologies.
Experience supporting data engineering solutions in secure or multi-environment enterprise settings.
Relevant cloud or data engineering certifications.

Interview Process
Video interview required and may include a technical assessment.
Candidates should be prepared to discuss:
Hands-on experience with Databricks, PySpark, Python, and SQL.
Examples of pipelines or data applications built from the ground up.
Large-scale data challenges solved in enterprise environments.
Experience with cloud, ETL/ELT, and enterprise data engineering best practices.

## Description

Data Engineering and Pipeline Development
Design, develop, and maintain scalable batch and streaming data pipelines supporting enterprise analytics and data operations.
Build and optimize Databricks and PySpark workflows for ingestion, transformation, validation, and processing of large datasets.
Develop robust ETL/ELT solutions across structured and unstructured data sources, including APIs, relational databases, NoSQL databases, and cloud storage.
Support data modeling, pipeline orchestration, data quality, lineage, and performance tuning across the data lifecycle.
Implement and maintain cloud-native data engineering solutions in environments such as AWS, Azure, or GCP.

Collaboration and Delivery
Collaborate with engineers, analysts, data scientists, and other stakeholders to support reporting, analytics, and AI/ML data preparation workflows.
Integrate data pipelines into DevSecOps and CI/CD workflows and support secure, repeatable deployment practices.
Troubleshoot pipeline failures, improve observability, and optimize the performance and reliability of enterprise data services.
Contribute to technical documentation, engineering standards, and best practices for enterprise-scale data development.
Operate effectively within Agile or SAFe delivery environments supporting mission systems.