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)