Data Engineer

Parsons Corporation•Reston, VA

About The Position

Parsons is looking for an amazingly talented Data Engineer to join our team! In this role, you will support the design, implementation, and sustainment of the data plane for a large-scale distributed system spanning microservices, event processing, search/analytics, enterprise reporting, and operational telemetry. You will work across both cloud-native and constrained edge-style deployments, helping ensure data is collected, transformed, moved, searched, and visualized reliably even in challenging operating environments. This role is not a traditional database administrator position. Instead, it is focused on data engineering across distributed microservices, event pipelines, search/analytics platforms, enterprise data integration, and resilient/manual data movement workflows.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or related technical field. 4 Additional years of experience can substitute for a degree.
10+ years of software and/or data engineering
Strong experience in data engineering for distributed applications and microservices-based systems
Hands-on experience with: Java-based service ecosystems MongoDB Elasticsearch / ELK RabbitMQ or similar message/event platforms
Good understanding of distributed systems concepts including: eventual consistency retries and idempotency partition tolerance and degraded/offline operation data reconciliation and replay
Experience developing and supporting data pipelines across both operational systems and analytics/reporting environments
Experience designing robust data movement mechanisms, including fallback/manual transfer workflows for disrupted environments
Experience with Power BI and/or Power Automate integration pipelines, including data shaping and feed preparation for dashboards and workflow automation
Experience working with telemetry/log/metric pipelines and operational dashboarding concepts
Ability to write clean technical documentation for schemas, data flows, and operating procedures
Strong oral and written communication skills and the ability to work cooperatively and effectively as a team member
Domestic or international travel may be required.
An active Top Secret SCI security clearance is required for this position.

Nice To Haves

Master’s degree in Data Engineering, Computer Science, Analytics, or related field
Experience with service platform tooling such as Consul, Nomad, and Vault
Familiarity with legacy-to-modern data transition patterns, including relational to document-oriented migration approaches
Experience supporting systems deployed in constrained environments with intermittent connectivity and variable infrastructure quality
Experience with PostgreSQL and Grails/Java legacy systems in migration or sustainment contexts
Experience with search engineering, semantic search, or cognitive search implementations using Elasticsearch
Experience building data feeds that support operational dashboards, executive reporting, and data science workflows
Familiarity with observability tooling transitions and telemetry normalization across multiple sources
Ability to identify data architecture improvements, develop prototypes, and help build the case for operational enhancements
Experience training users or technical teams as new data capabilities move into production

Responsibilities

Designing, developing, and maintaining the data plane for a distributed microservices ecosystem consisting of: approximately 25 core Java microservices approximately 25 ancillary integration microservices connecting to external systems
Supporting data flows across a modern service platform built around: MongoDB for operational data storage Elasticsearch / ELK for analytics, search, cognitive/semantic search use cases, and telemetry analysis RabbitMQ for event-driven processing and message distribution service orchestration and security components such as Consul, Nomad, and Vault
Engineering data solutions that can operate across a wide range of deployment models: very small, constrained environments (e.g., a few bare-metal small-form-factor servers) larger hybrid/cloud-native environments that can scale to hundreds of nodes
Building and optimizing data pipelines that support: operational application data flows telemetry ingestion (hot/warm/cold paths) dashboard/reporting data feeds enterprise data integration across inventory, asset, and financial systems
Developing resilient mechanisms for manual or semi-manual data extraction, transfer, and re-ingestion when automated connectivity is degraded or unavailable due to technical, physical, or geopolitical disruptions
Supporting data migration activities between legacy and modernized platforms: legacy platform built with Java/Grails using PostgreSQL current distributed platform using MongoDB working within existing migration processes and improving tooling, reliability, and observability where appropriate
Building and maintaining integrations that feed Power BI dashboards, Power Automate workflows, and downstream data science/analytics use cases
Enabling operational visibility through telemetry engineering: ingesting and transforming logs, events, and metrics supporting observability pipelines based on ELK and current/transitioning monitoring tooling helping shape future-state consolidation toward a more ELK-centric observability model
Collaborating closely with software engineers, platform engineers, SRE/operations, data scientists, and enterprise stakeholders to operationalize data and deliver reliable insights
Documenting data models, interfaces, schemas, transformations, operational procedures, and recovery workflows