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Salary: USD 190,000 - 240,000 / annual
Ready is a broadband infrastructure monitoring platform supporting the BEAD (Broadband Equity, Access, and Deployment) program. The company is expanding its data engineering team to build and maintain the data infrastructure powering multi-state broadband programs.
You will work closely with the Head of Data and cross-functional teams as a hands-on Senior Data Engineer, spending most of your time building Airflow DAGs, designing data models, and ensuring data moves reliably at scale.
Key responsibilities include:
- Design, implement, and maintain scalable data infrastructure on AWS (S3, Athena, RDS/PostgreSQL, ECS, Lambda, Secrets Manager, CloudWatch)
- Own data lake and warehouse architecture, including partitioning strategies, storage optimization, and data lifecycle management
- Build and maintain production-grade Apache Airflow DAGs for ingestion, transformation, and export workflows
- Build and maintain robust DBT pipelines with data quality checks and well-structured data modeling
- Design and maintain robust database schemas for multi-state, multi-tenant program data
- Write and optimize SQL queries across PostgreSQL, Redshift, and Athena
- Develop reusable data models, utilities, and shared Python packages
- Design data models and infrastructure for large-scale time-series and event-based data management
- Work with vector databases to support AI-powered features and semantic search
- Integrate LLM workflows into ELT pipelines using AWS Bedrock, LangChain, and related frameworks
- Build AI-assisted data comparison, validation, and enrichment pipelines
- Design and build automated data QA systems to validate quality, completeness, and consistency
- Implement cleansing and reconciliation routines for complex multi-source ingestion flows
- 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
- Partner with product and engineering teams to translate requirements into technical architecture
- Evaluate and recommend data tools, frameworks, and infrastructure choices
- Ensure observability across the data platform with monitoring, alerting, and incident resolution
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 balancing cost and performance
- Strong proficiency in SQL (PostgreSQL, Athena/Presto) and Python; 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 (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; ability to make complex infrastructure decisions legible to non-engineers
- Comfortable working with ambiguity and making decisions following first-principle thinking
- Comfortable working across multiple timezones
- Experience managing data for SaaS platforms is a plus
- Experience with large-scale spatiotemporal data pipelines (PostGIS, spatial indexing, tiling) is a plus
