Overview

We are seeking a Senior Data Engineer with deep expertise in data pipeline architecture, legacy system modernization, and cloud-native data platforms. The ideal candidate brings hands-on experience migrating monolithic, legacy applications and databases to modern microservices architectures and managed cloud services — while maintaining data integrity and minimizing disruption to live operations. Strong proficiency in Java and Node.js is essential, particularly in the context of rewriting or integrating legacy systems.

What You’ll Do

• Assess, re-architect, and rebuild legacy databases — migrating aging or end-of-life systems to modern platforms with minimal disruption to mission operations.
• Lead or contribute to efforts breaking monolithic applications into microservices, with a focus on how data flows, ownership, and contracts change across service boundaries.
• Design, build, and maintain production data pipelines and ETL/ELT workflows across batch and streaming contexts.
• Implement data quality, validation, and governance practices to ensure reliability and trustworthiness of data at every stage of the pipeline.
• Collaborate with cross-functional teams and customers to gather requirements and translate them into robust data architecture decisions.
• Work alongside Java and Node.js backend engineers — and write in both yourself — to modernize data access layers, event-driven integrations, and service APIs.
• Document data flows, architecture decisions, lineage, and operational runbooks to support long-term maintainability.
• Mentor junior engineers and contribute to technical standards, patterns, and best practices across the team.

Skill Sets

Data Engineering & Architecture

• Strong proficiency in SQL, including schema design, query optimization, indexing strategies, and large-scale data migration.
• Experience designing and building production-grade ETL/ELT pipelines, including error handling, idempotency, and observability.
• Solid understanding of data modeling, warehousing concepts, and schema design for both OLTP and OLAP workloads.
• Hands-on experience with data quality frameworks, validation rules, and governance practices.
• Ability to design data contracts between services and establish clear ownership boundaries in a distributed system.

Legacy Modernization & Microservices

• Demonstrated experience leading or contributing to legacy system rewrites or modernization efforts — particularly migrations from monolithic architectures to microservices.
• Deep understanding of the data challenges that accompany monolith decomposition: shared databases, tight coupling, event sourcing, and eventual consistency.
• Experience designing and implementing the Strangler Fig pattern or other incremental migration strategies that allow systems to evolve without big-bang cutover.
• Practical knowledge of anti-patterns in legacy systems and pragmatic strategies for addressing them without full rewrites.
• Comfort working in brownfield codebases alongside existing Java and Node.js systems, including reading and refactoring code written by others.

Java & Node.js

• Strong Java experience, particularly in the context of enterprise or backend systems — including Spring Boot, persistence layers (JPA/Hibernate), and data integration patterns.
• Solid Node.js experience, including building data access layers, REST or event-driven APIs, and integrations with cloud services and databases.
• Ability to read, reason about, and modernize legacy Java codebases: identifying data access patterns, untangling business logic from persistence, and preparing for service extraction.
• Experience writing data migration scripts, transformation utilities, or CLI tooling in Java, Node.js, or both.

AWS & Cloud Data Services

• Proficiency with AWS data and compute services: RDS, Aurora, DynamoDB, S3, Glue, Lambda, Kinesis, MSK (Kafka), Redshift, Athena, and Step Functions.
• Experience using AWS CDK or CloudFormation to provision and manage data infrastructure as code.
• Strong AWS IAM knowledge as it applies to data workloads — securing access to S3 buckets, RDS instances, Glue jobs, and cross-account data sharing.
• Familiarity with containerization (Docker) and how it applies to data pipeline deployments and reproducible environments.

Collaboration & Communication

• Ability to collaborate effectively with cross-functional teams, including product, backend engineering, DevOps, and operations.
• Clear communicator capable of documenting complex data systems for both technical and non-technical audiences.
• Ability to establish and maintain effective working relationships with clients, employees, vendors, and the public.
• Continuous learning and adaptation to emerging technologies, particularly in the data engineering and cloud-native space.

Minimum Education Requirements & Certifications

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field (industry experience may be substituted).
• Relevant certifications in data engineering, cloud platforms, or specific technologies.
• AWS certifications (e.g., Data Engineer – Associate, Solutions Architect, Database Specialty) are a strong plus.

Experience Preferred

• 7+ years of professional data engineering or full-stack engineering experience with a strong data focus.
• Proven track record of rebuilding or migrating legacy databases — preserving data integrity and minimizing downtime during transition.
• Direct experience migrating a monolithic application to a microservices architecture, with specific involvement in the data layer.
• Hands-on experience modernizing systems built in Java, Node.js, or both.
• Experience with PostgreSQL at a production scale, including schema conversion, migration tooling, and performance tuning.
• Prior experience supporting federal or government contracts is a plus.
• Experience with at least one major cloud platform — AWS strongly preferred.

Ideal Technology Experience

Java · Node.js · TypeScript · SQL · PostgreSQL · AWS (RDS, Aurora, Glue, Lambda, Kinesis, S3, Redshift, Athena) · AWS CDK / CloudFormation · Docker · Apache Kafka / MSK · dbt or similar transformation tooling

Primary Duties & Responsibilities

The successful candidate will be responsible for the following:

• Assess existing legacy databases and data systems; develop and execute migration plans to modern, cloud-native platforms.
• Design and implement data pipeline architectures that are scalable, observable, and fault-tolerant.
• Actively participate in monolith-to-microservices decomposition efforts, owning data strategy for service extraction and event-driven integration.
• Write and maintain production-quality Java and Node.js code for data services, migration tooling, and integrations.
• Define and enforce data quality, validation, and governance standards across all pipeline stages.
• Collaborate with product managers and stakeholders to gather data requirements and translate them into technical designs.
• Implement security and data protection measures, including proper IAM scoping and encryption at rest and in transit.
• Participate in code and design reviews to maintain quality and ensure adherence to architectural standards.
• Troubleshoot, debug, and optimize data pipelines and queries for performance and reliability.
• Document data architecture, lineage, flow diagrams, and runbooks for operational clarity.
• Stay current on industry trends and recommend appropriate technologies, patterns, and tools.
• Mentor junior engineers and contribute to the team’s overall technical growth and standards.
• Maintain a professional, collaborative presence across all client and internal communications.
• Remain agile and responsive to evolving Company and Client needs.