Job description

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. About your role at Ready ⚡️ - 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 A bit about you 🥇 - 5+ years of data engineering experience, with a track record of owning and operationally supporting production systems end-to-end - Proven ability to evaluate technical trade-offs and make pragmatic architecture decisions that balance cost and performance - Strong proficiency in SQL (PostgreSQL, Athena/Presto) and Python; you write production-quality code - Deep hands-on experience with AWS data services (S3, Athena, RDS, ECS, Lambda, IAM, CloudWatch) - Experience with Apache Airflow or similar orchestration tools in production environments - Experience with large-scale time-series, event-based, or streaming data systems - Experience with version control with Github/Subversion/GitLab/Mercurial - Familiarity with vector databases and LLM integration patterns (LangChain, AWS Bedrock, or similar) - Experience building data quality frameworks or automated validation systems - Demonstrated ability to mentor engineers and contribute to team culture - Excellent communication skills — you can make complex infrastructure decisions legible to non-engineers - Comfortable working with ambiguity and make decisions following first-principle - Comfortable working across multiple timezone - Experience managing data for SaaS platforms is a plus - Experience with large-scale spatiotemporal data pipelines (PostGIS, spatial indexing, tiling) is a plus About Ready 🚀 - Creative problem solvers approaching a legacy industry with a new point of view - Humble but ambitious, knowledgeable but curious, persistent but not obnoxious - Concise and effective in written and spoken communication - Comfortable working remotely - One team, one dream About what you get… - Competitive salary plus meaningful equity upside; salary range is $190,000-$240,000 - 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 all convene 2-3x / year for [optional, encouraged] retreats - We’re actively shaping our benefits program: have a say in which benefits matter to you - The charter to realize a market that is set to receive $65 billion in grant funding across the United States Equal Employment Opportunity At Ready, we believe that the best ideas come from people with different backgrounds, experiences, and perspectives. We are committed to creating an inclusive workplace where everyone has the opportunity to do their best work. Ready is proud to be an Equal Opportunity Employer. We make all employment decisions based on qualifications, merit, and business needs, and do not discriminate on the basis of race, color, religion, creed, sex, sexual orientation, gender identity or expression, pregnancy, national origin, ancestry, age, disability, genetic information, veteran or military status, marital status, or any other status protected by applicable law. If you need a reasonable accommodation during the hiring process, please let us know — we're happy to work with you to ensure an accessible and equitable experience.

Requirements

Must-haves

About your role at Ready ⚡️
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

Nice-to-haves

Experience managing data for SaaS platforms is a plus
Experience with large-scale spatiotemporal data pipelines (PostGIS, spatial indexing, tiling) is a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

awsgcpsqlpythonaillmml

Platform: ashby
Effort: quick

Benefits

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