The ad says“This is a hands-on engineering role — expect to spend most of your time building Airflow DAGs, designing data models and making data move reliably at scale”
The ad says“Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)”
The ad says“We’re looking for a Senior Data Engineer who thrives in complex pipeline environments, is comfortable across AWS and GCP, and can bring structure and reliability to large-scale government data programs”
The ad says“Integrate LLM workflows into ELT pipelines using AWS Bedrock, LangChain, and related frameworks”
The ad says“Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)”
The ad says“This is a hands-on engineering role — expect to spend most of your time building Airflow DAGs, designing data models and making data move reliably at scale”
The ad says“Build and maintain robust DBT pipelines with data quality checks and well-structured data modeling”
The ad says“Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)”
The ad says“We’re looking for a Senior Data Engineer who thrives in complex pipeline environments, is comfortable across AWS and GCP, and can bring structure and reliability to large-scale government data programs”
The ad says“Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)”
The ad says“Integrate LLM workflows into ELT pipelines using AWS Bedrock, LangChain, and related frameworks”
The ad says“Integrate LLM workflows into ELT pipelines using AWS Bedrock, LangChain, and related frameworks”
The ad says“Experience with MLflow, deploying production level ML pipelines”
The ad says“Write and optimize SQL queries across PostgreSQL, Redshift and Athena for analytical and operational workloads”
The ad says“Develop reusable data models, utilities, and shared Python packages that raise the bar for the whole team”
The ad says“Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)”
The ad says“Write and optimize SQL queries across PostgreSQL, Redshift and Athena for analytical and operational workloads”
The ad says“Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)”
The ad says“Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)”
The ad says“Write and optimize SQL queries across PostgreSQL, Redshift and Athena for analytical and operational workloads”
The ad says“Work with vector databases to support AI-powered features and semantic search capabilities”
Values
The ad says“Experience with version control with Github/Subversion/GitLab/Mercurial”
The ad says“Experience with large-scale spatiotemporal data pipelines (PostGIS, spatial indexing, tiling) is a plus”
Ready is expanding its data engineering team to support rapid growth across our broadband infrastructure monitoring platform and the BEAD (Broadband Equity, Access, and Deployment) program
We’re looking for a Senior Data Engineer who thrives in complex pipeline environments, is comfortable across AWS and GCP, and can bring structure and reliability to large-scale government data programs
You’ll work closely with the Head of Data and a cross-functional team to build, optimize, and maintain the data infrastructure that powers multi-state broadband programs
This is a hands-on engineering role — expect to spend most of your time building Airflow DAGs, designing data models and making data move reliably at scale
Partner with product and engineering teams to understand requirements and translate them into the right technical architecture — balancing cost, performance, and long-term maintainability
Evaluate and recommend data tools, frameworks, and infrastructure choices with a pragmatic, product-first mindset
Contribute to roadmap discussions and help the team make informed build-vs-buy decisions
Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)
Own data lake and warehouse architecture — partitioning strategies, storage optimization, and data lifecycle management
Build and maintain production-grade Apache Airflow DAGs for ingestion, transformation, and export workflows
Ensure observability across the data platform — monitoring, alerting, and fast incident resolution
Build and maintain robust DBT pipelines with data quality checks and well-structured data modeling
Design and maintain robust database schemas to support multi-state, multi-tenant program data
Write and optimize SQL queries across PostgreSQL, Redshift and Athena for analytical and operational workloads
Develop reusable data models, utilities, and shared Python packages that raise the bar for the whole team
Design data models and infrastructure for large-scale time-series and event-based data management at scale
Implement efficient ingestion, storage, and retrieval patterns for high-frequency temporal datasets
Work with vector databases to support AI-powered features and semantic search capabilities
Integrate LLM workflows into ELT pipelines using AWS Bedrock, LangChain, and related frameworks
Build AI-assisted data comparison, validation, and enrichment pipelines that run reliably in production
Experience with MLflow, deploying production level ML pipelines
Stay current on the evolving AI/ML tooling landscape and bring relevant innovations to the team
Design and build automated data QA systems to validate quality, completeness, and consistency across datasets
Implement cleansing and reconciliation routines for complex multi-source ingestion flows
Proactively monitor pipelines and resolve issues before they reach downstream consumers or clients
Mentor junior and mid-level data engineers — through code reviews, pair programming, and architectural guidance
Help establish team standards for code quality, testing, and documentation
Foster a culture of ownership, curiosity, and continuous improvement on the data team
Benefits
Meaningful equity upside
Competitive (and ever expanding) benefits for employees and dependents
Opportunities to learn and grow – all things startups
A chance to play a role in defining the roadmap as we pursue a bold vision and and a big goal
Work from anywhere you want, as long as you can get great internet (and your work here at Ready helps make this true in more places)
To get away - we convene 2-3x / year for [optional, encouraged] retreats
We’re actively shaping our benefits program: have a say in which benefits matter to you
Preferred experience
Comfortable working with ambiguity and make decisions following first-principle
Demonstrated ability to mentor engineers and contribute to team culture
Experience with large-scale spatiotemporal data pipelines (PostGIS, spatial indexing, tiling) is a plus
Experience managing data for SaaS platforms is a plus
Deep hands-on experience with AWS data services (S3, Athena, RDS, ECS, Lambda, IAM, CloudWatch)
Comfortable working across multiple timezone
Experience building data quality frameworks or automated validation systems
Experience with version control with Github/Subversion/GitLab/Mercurial
Experience with large-scale time-series, event-based, or streaming data systems
Experience with Apache Airflow or similar orchestration tools in production environments
Excellent communication skills — you can make complex infrastructure decisions legible to non-engineers
Familiarity with vector databases and LLM integration patterns (LangChain, AWS Bedrock, or similar)
Strong proficiency in SQL (PostgreSQL, Athena/Presto) and Python; you write production-quality code
Proven ability to evaluate technical trade-offs and make pragmatic architecture decisions that balance cost and performance
5+ years of data engineering experience, with a track record of owning and operationally supporting production systems end-to-end
