Senior Data Engineer

Ready • San Francisco, CA

15d • $190,000 - $240,000 • Remote

About The Position

Ready is expanding its data engineering team to support rapid growth across its broadband infrastructure monitoring platform and the BEAD (Broadband Equity, Access, and Deployment) program. The role involves working 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 focused on building Airflow DAGs, designing data models, and ensuring reliable data movement at scale.

Requirements

• 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

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

Responsibilities

• 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

• Competitive salary plus 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
• Convene 2-3x / year for retreats
• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed