Data Engineering Software Developer (SME)

Washington, DC, US

Actively hiring, first seen by us 2 days ago

Pay: $235,000–265,000/yearAnnual period assumed — pay source
Job Type: Full-time Target Salary Range*: $235,000 - $265,000 *This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary
Read the full posting
Work setting: Not stated
Employment: Full-time — employment source
Clearance: Top Secret/SCI Job Type: Full-time Target Salary Range*: $235,000 - $265,000
Read the full posting

Before you apply

Work authorization: Citizens onlyActive TS/SCI security clearance

What you’ll work on

Full posting

Design, build and maintain scalable ETL/ELT data pipelines from disparate sources
Unify data from multiple network operations systems into one platform
Create and maintain Kafka, Elastic and Logstash ingestion pipelines
Optimize ingestion throughput, query performance and storage across Elastic and Kafka
Support CI/CD pipelines and document platform configurations and runbooks

From the employer’s posting

Design, build, and maintain scalable and reliable data systems and pipelines to ingest, process, and transform large, complex, and disparate data sources

Unify and integrate data from multiple network operations systems into a consistent and accessible platform.

Create and maintain Kafka, Elastic, and Logstash pipelines supporting enterprise data ingestion and processing.

Analyze and optimize ingestion throughput, query performance, indexing strategies, and storage efficiency across Elastic and Kafka platforms.

Support CI/CD pipelines for automated build, test, and deployment activities.

What you’ll bring

All qualifications

Core experience

Bachelor’s degree in Computer Science, Engineering, or a related technical discipline with 12+ years of relevant experience, or
Hands-on experience designing, building, and maintaining ETL/ELT data pipelines and integrating multiple disparate data sources.
Proficiency with data engineering tools and languages such as Python, Java, SQL, and shell scripting.
Master’s degree in Computer Science, Engineering, or a related technical discipline with 10+ years of relevant experience
Experience with data warehouse and/or data lake technologies such as AWS Redshift, Amazon S3, Hadoop, Snowflake, or similar platforms.
Knowledge of data security, access controls, and compliance frameworks, including DoD, DISA, RMF, and STIG requirements.

Preferred experience

Experience supporting DISA, DISN, or other DoD network environments.
Experience developing and deploying software applications that meet DoD security standards, including applicable STIGs.
Knowledge of network security, encryption, and access controls within classified environments.
Experience with big data technologies and platforms such as Kafka, Spark, NiFi, Hadoop, Elasticsearch, Logstash, Databricks, and ELK Stack.

Qualification wording

Bachelor’s degree in Computer Science, Engineering, or a related technical discipline with 12+ years of relevant experience, or

Hands-on experience designing, building, and maintaining ETL/ELT data pipelines and integrating multiple disparate data sources. [Required]

Proficiency with data engineering tools and languages such as Python, Java, SQL, and shell scripting. [Required]

Master’s degree in Computer Science, Engineering, or a related technical discipline with 10+ years of relevant experience

Experience with data warehouse and/or data lake technologies such as AWS Redshift, Amazon S3, Hadoop, Snowflake, or similar platforms. [Required]

Knowledge of data security, access controls, and compliance frameworks, including DoD, DISA, RMF, and STIG requirements. [Required]

Experience supporting DISA, DISN, or other DoD network environments.

Experience developing and deploying software applications that meet DoD security standards, including applicable STIGs.

Knowledge of network security, encryption, and access controls within classified environments.

Experience with big data technologies and platforms such as Kafka, Spark, NiFi, Hadoop, Elasticsearch, Logstash, Databricks, and ELK Stack.

Education & alternatives

Education - Bachelor’s degree in Computer Science, Engineering, or a related technical discipline with 12+ years of relevant experience, or - Master’s degree in Computer Science, Engineering, or a related technical discipline with 10+ years of relevant experience

- Bachelor’s degree in Computer Science, Engineering, or a related technical discipline with 12+ years of relevant experience, or - Master’s degree in Computer Science, Engineering, or a related technical discipline with 10+ years of relevant experience Experience

Apply at Amatriot Group

Posting record

Report an error

Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 1, 20262 days ago
Recorded sightings: 4
Last seen by us: Nov 4, 2026
Employer says posted: Sep 25, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

Higher than most similar jobs

$235K–$265K against $127K–$190K, the middle half of what similar US postings state.

From 2,133 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 2

Early. Similar jobs usually come down between day 5 and day 54.

From 10,736 similar postings that came down. Coming down is not always a hire.

Amatriot Group is hiring

21 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Java
JavaScript
Python
SQL
AWS
Azure
Databricks
Elasticsearch
GCP
Grafana
Hadoop
Kafka
Kibana
Kubernetes
Logstash
Redshift
S3
Snowflake
Spark
React

Where the posting names them

Skills - Proficiency with data engineering tools and languages such as Python, Java, SQL, and shell scripting. [Required] - Knowledge of data security, access controls, and compliance frameworks, including DoD, DISA, RMF, and STIG requirements. [Required]

- Develop and manage data warehouses and data lakes supporting analytics, reporting, and operational dashboards. - Support Kibana visualizations and dashboards using React, JavaScript, and HTML. - Apply automation and engineering best practices to improve platform scalability, maintainability, and reliability.

The Data Engineering Software Developer (SME) designs, builds, integrates, and maintains scalable data systems and pipelines that support analytics, reporting, and operational insight across complex enterprise environments. This role serves as a subject matter expert for data ingestion, transformation, integration, platform optimization, and secure data engineering within DoD and DISA environments. The position develops and supports solutions using Confluent Kafka, Elastic Stack, AWS, Red Hat OpenShift/Kubernetes, data warehouses, and data lakes. The role also supports automation, CI/CD, observability, visualization, technical documentation, and collaboration across software, data, analytics, and network operations teams. Key Responsibilities

Data Platforms and Software Development - Implement data solutions using Confluent Kafka, Elastic Stack, AWS, and Red Hat OpenShift/Kubernetes. - Develop and manage data warehouses and data lakes supporting analytics, reporting, and operational dashboards.

- Hands-on experience designing, building, and maintaining ETL/ELT data pipelines and integrating multiple disparate data sources. [Required] - Experience with data warehouse and/or data lake technologies such as AWS Redshift, Amazon S3, Hadoop, Snowflake, or similar platforms. [Required] - Experience with Elastic Stack, including Elasticsearch, Logstash, and Kibana, and/or Kafka for data ingestion and processing. [Required]

- Experience applying big data technologies to text mining, summarization, search, and entity extraction. - Experience with cloud-based data platforms, including AWS, Azure, GCP, or AWS GovCloud. - Experience with cloud-integrated platforms and Infrastructure as Code, including networking and security policies.

- Knowledge of network security, encryption, and access controls within classified environments. - Experience with big data technologies and platforms such as Kafka, Spark, NiFi, Hadoop, Elasticsearch, Logstash, Databricks, and ELK Stack. - Experience applying big data technologies to text mining, summarization, search, and entity extraction.

- Experience with data warehouse and/or data lake technologies such as AWS Redshift, Amazon S3, Hadoop, Snowflake, or similar platforms. [Required] - Experience with Elastic Stack, including Elasticsearch, Logstash, and Kibana, and/or Kafka for data ingestion and processing. [Required] - Experience with Linux/UNIX system administration and automation. [Required]

- Experience with configuration management technologies such as Ansible, Puppet, or Chef. - Experience with monitoring, alerting, and observability technologies such as Prometheus, Grafana, and Elastic. - Experience with business intelligence and visualization tools such as Kibana.

- Unify and integrate data from multiple network operations systems into a consistent and accessible platform. - Create and maintain Kafka, Elastic, and Logstash pipelines supporting enterprise data ingestion and processing. - Automate ETL/ELT processes, data validation, cleansing, monitoring, and related data-processing activities.

Performance and Platform Optimization - Analyze and optimize ingestion throughput, query performance, indexing strategies, and storage efficiency across Elastic and Kafka platforms. - Troubleshoot and resolve issues related to data ingestion, processing, storage, memory utilization, partitioning, and cluster performance.

- Experience with monitoring, alerting, and observability technologies such as Prometheus, Grafana, and Elastic. - Experience with business intelligence and visualization tools such as Kibana. - Experience developing and maintaining technical documentation and operational runbooks.

- Experience with cloud-integrated platforms and Infrastructure as Code, including networking and security policies. - Familiarity with Kubernetes deployment, platform upgrades, and patching. - Experience with configuration management technologies such as Ansible, Puppet, or Chef.

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About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025: $139,500
Expected change, 2025 to 2035: 9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

Location: Washington, DC 100% Onsite
Clearance: Top Secret/SCI
Job Type: Full-time
Target Salary Range*: $235,000 - $265,000

*This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary

Position Overview

The Data Engineering Software Developer (SME) designs, builds, integrates, and maintains scalable data systems and pipelines that support analytics, reporting, and operational insight across complex enterprise environments. This role serves as a subject matter expert for data ingestion, transformation, integration, platform optimization, and secure data engineering within DoD and DISA environments.

The position develops and supports solutions using Confluent Kafka, Elastic Stack, AWS, Red Hat OpenShift/Kubernetes, data warehouses, and data lakes. The role also supports automation, CI/CD, observability, visualization, technical documentation, and collaboration across software, data, analytics, and network operations teams.

Key Responsibilities

Data Pipeline Development and Integration

Design, build, and maintain scalable and reliable data systems and pipelines to ingest, process, and transform large, complex, and disparate data sources for analytics and reporting.
Unify and integrate data from multiple network operations systems into a consistent and accessible platform.
Create and maintain Kafka, Elastic, and Logstash pipelines supporting enterprise data ingestion and processing.
Automate ETL/ELT processes, data validation, cleansing, monitoring, and related data-processing activities.

Data Platforms and Software Development

Implement data solutions using Confluent Kafka, Elastic Stack, AWS, and Red Hat OpenShift/Kubernetes.
Develop and manage data warehouses and data lakes supporting analytics, reporting, and operational dashboards.
Support Kibana visualizations and dashboards using React, JavaScript, and HTML.
Apply automation and engineering best practices to improve platform scalability, maintainability, and reliability.

Performance and Platform Optimization

Analyze and optimize ingestion throughput, query performance, indexing strategies, and storage efficiency across Elastic and Kafka platforms.
Troubleshoot and resolve issues related to data ingestion, processing, storage, memory utilization, partitioning, and cluster performance.
Identify and resolve performance bottlenecks affecting data availability and downstream analytics.
Optimize infrastructure for performance, scalability, and cost effectiveness.

Security, Compliance, and DevOps

Ensure data quality, accuracy, security, and compliance with applicable DoD and DISA requirements.
Apply appropriate data security, access-control, and compliance practices.
Support CI/CD pipelines for automated build, test, and deployment activities.
Apply version control and DevOps practices to data engineering and software development workflows.

Collaboration and Documentation

Collaborate with data analysts, visualization engineers, software developers, and other stakeholders to deliver integrated and actionable data solutions.
Document data architecture, integration patterns, platform configurations, and operational procedures.
Develop and maintain technical documentation and runbooks supporting data platforms and pipelines.

Qualifications

Education

Bachelor’s degree in Computer Science, Engineering, or a related technical discipline with 12+ years of relevant experience, or
Master’s degree in Computer Science, Engineering, or a related technical discipline with 10+ years of relevant experience

Experience

Hands-on experience designing, building, and maintaining ETL/ELT data pipelines and integrating multiple disparate data sources. [Required]
Experience with data warehouse and/or data lake technologies such as AWS Redshift, Amazon S3, Hadoop, Snowflake, or similar platforms. [Required]
Experience with Elastic Stack, including Elasticsearch, Logstash, and Kibana, and/or Kafka for data ingestion and processing. [Required]
Experience with Linux/UNIX system administration and automation. [Required]
Experience with CI/CD pipelines, containerized pipelines, version control technologies such as Git and Bitbucket, and DevOps practices. [Required]
Familiarity with network operations data, including logs, metrics, events, ticketing, CMDB data, and related data models.

Skills

Proficiency with data engineering tools and languages such as Python, Java, SQL, and shell scripting. [Required]
Knowledge of data security, access controls, and compliance frameworks, including DoD, DISA, RMF, and STIG requirements. [Required]
Strong communication and documentation skills supporting collaboration and knowledge sharing. [Required]

Certifications

Current Security+ certification or higher/equivalent certification. [Required]

Clearance

Active TS/SCI security clearance. [Required]

Other Requirements

Willingness and ability to complete and maintain additional customer suitability screenings throughout employment. [Required]

Preferred Qualifications

Experience supporting DISA, DISN, or other DoD network environments.
Experience developing and deploying software applications that meet DoD security standards, including applicable STIGs.
Knowledge of network security, encryption, and access controls within classified environments.
Experience with big data technologies and platforms such as Kafka, Spark, NiFi, Hadoop, Elasticsearch, Logstash, Databricks, and ELK Stack.
Experience applying big data technologies to text mining, summarization, search, and entity extraction.
Experience with cloud-based data platforms, including AWS, Azure, GCP, or AWS GovCloud.
Experience with cloud-integrated platforms and Infrastructure as Code, including networking and security policies.
Familiarity with Kubernetes deployment, platform upgrades, and patching.
Experience with configuration management technologies such as Ansible, Puppet, or Chef.
Experience with monitoring, alerting, and observability technologies such as Prometheus, Grafana, and Elastic.
Experience with business intelligence and visualization tools such as Kibana.
Experience developing and maintaining technical documentation and operational runbooks.
Strong understanding and practical experience with Agile methodologies, including Scrum and SAFe.
Experience with Atlassian Jira and Confluence.
Confluent Developer certification.
Elastic Certified Engineer certification.
Experience working remotely with geographically dispersed teams and within matrixed organizations.
Experience combining software development, integration, and data engineering practices.
Experience applying knowledge of system architecture, networking, and centralized logging using ELK to support data transformation initiatives.

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