Data Engineering Software Developer Senior

Washington, DC, US

Actively hiring, first seen by us 2 days ago

Pay: $175,000–200,000/yearAnnual period assumed — pay source
Job Type: Full-time Target Salary Range*: $175,000 - $200,000 *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
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Work setting: Not stated
Employment: Full-time — employment source
Clearance: Top Secret/SCI Job Type: Full-time Target Salary Range*: $175,000 - $200,000
Read the full posting

Before you apply

Work authorization: Citizens onlyActive TS/SCI security clearance

What you’ll work on

Full posting

Build and maintain Kafka, Elastic, and Logstash pipelines for enterprise data ingestion
Unify data from multiple network operations systems into one accessible platform
Automate ETL/ELT processes, data validation, and cleansing
Tune ingestion throughput, query performance, and cluster utilization on Elastic and Kafka
Support Kibana visualizations and dashboards

From the employer’s posting

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

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

Automate ETL/ELT processes, data validation, cleansing, monitoring, and related data-engineering activities.

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

Support Kibana visualizations and dashboards.

What you’ll bring

All qualifications

Core experience

Bachelor’s degree in Computer Science, Engineering, or a related technical discipline with 8+ years of relevant experience.
Hands-on experience designing, building, and maintaining ETL/ELT data pipelines and integrating multiple disparate data sources.
Proficiency with data engineering technologies and languages such as Python, Java, SQL, and shell scripting.
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.
Experience with Elastic Stack, including Elasticsearch, Logstash, and Kibana, and/or Kafka for data ingestion and processing.

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 8+ years of relevant experience. [Required]

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

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

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 with Elastic Stack, including Elasticsearch, Logstash, and Kibana, and/or Kafka for data ingestion and processing. [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 8+ years of relevant experience. [Required] Experience

Apply at Amatriot Group

Posting record

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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

In line with similar jobs

$175K–$200K 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 technologies 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]

- Support Kibana visualizations and dashboards. - Support visualization capabilities using React, JavaScript, and HTML. - Collaborate with data analysts, visualization engineers, and other stakeholders to deliver integrated and actionable data solutions.

The Senior Data Engineer designs, builds, integrates, and sustains scalable data systems and pipelines that support analytics, reporting, and operational insight across complex enterprise environments. This role focuses on ingesting, processing, transforming, and unifying large and disparate data sources while ensuring data quality, security, performance, and compliance with DoD and DISA requirements. The Senior Data Engineer works with Confluent Kafka, Elastic Stack, AWS, Red Hat OpenShift/Kubernetes, data warehouses, and data lakes to deliver reliable and efficient data solutions. The role also supports automation, CI/CD, platform optimization, visualization capabilities, and collaboration across data, software, and network operations teams. Key Responsibilities

Data Platforms and Architecture - 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 to support enterprise data ingestion and processing. - Automate ETL/ELT processes, data validation, cleansing, monitoring, and related data-engineering activities.

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

Visualization and Analytics Support - Support Kibana visualizations and dashboards. - Support visualization capabilities using React, JavaScript, and HTML.

- 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 deployments, 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*: $175,000 - $200,000

*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 Senior Data Engineer designs, builds, integrates, and sustains scalable data systems and pipelines that support analytics, reporting, and operational insight across complex enterprise environments. This role focuses on ingesting, processing, transforming, and unifying large and disparate data sources while ensuring data quality, security, performance, and compliance with DoD and DISA requirements.

The Senior Data Engineer works with Confluent Kafka, Elastic Stack, AWS, Red Hat OpenShift/Kubernetes, data warehouses, and data lakes to deliver reliable and efficient data solutions. The role also supports automation, CI/CD, platform optimization, visualization capabilities, and collaboration across data, software, and network operations teams.

Key Responsibilities

Data Pipeline Engineering and Integration

Design, build, and maintain scalable and reliable data systems and pipelines for ingesting, processing, and transforming 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 to support enterprise data ingestion and processing.
Automate ETL/ELT processes, data validation, cleansing, monitoring, and related data-engineering activities.

Data Platforms and Architecture

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.
Optimize data infrastructure for scalability, reliability, performance, and cost effectiveness.
Apply automation and engineering best practices to improve platform maintainability and operational efficiency.

Performance and Platform Optimization

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

Visualization and Analytics Support

Support Kibana visualizations and dashboards.
Support visualization capabilities using React, JavaScript, and HTML.
Collaborate with data analysts, visualization engineers, and other stakeholders to deliver integrated and actionable data solutions.

Security, Compliance, and DevOps

Ensure data quality, accuracy, security, and compliance with applicable DoD and DISA requirements.
Support CI/CD pipelines for automated build, test, and deployment activities.
Apply version control and DevOps practices to data-engineering workflows.
Support secure operation of data platforms through appropriate access controls and configuration standards.

Documentation and Collaboration

Document data architecture, integration patterns, platform configurations, and operational procedures.
Maintain technical documentation and runbooks supporting data platforms and pipelines.
Collaborate across software, integration, data, analytics, and network operations teams to support enterprise data initiatives.

Qualifications

Education

Bachelor’s degree in Computer Science, Engineering, or a related technical discipline with 8+ years of relevant experience. [Required]

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 technologies 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 deployments, 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, 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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