Senior Data Engineer

Who you are

• 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

What the job involves

• 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