Lead Data Engineer with Full Stack Development

Pay Range: $60 - $85/hr. The pay rate may differ depending on your skills, education, experience, and other qualifications.

Featured Benefits:

• Medical Insurance in compliance with the ACA.
• 401(k).
• Sick leave in compliance with applicable state, federal, and local laws.

Must Have

AWS – Dynamo, AWS – Lambda,  AWS - S3,  AWS - SNS/SQS, AWS – Redshift, AWS – Athena, Apache Iceberg, Airflow/AWS Step Functions, AWS – EMR, AWS – Glue, AWS - Quick Suite/Tableau, AWS SageMaker Studio, AWS Lake Formation, Python, Typscript, JavaScript (ES6), React.js, Grafana, InfluxDB, RBAC (Role-based Access Control), Machine Learning (ML), Artificial Intelligence (AI)

Profile: Strong cloud full-stack engineering experience, with deep knowledge across backend services, data platforms, APIs, and front-end analytics applications. You are comfortable designing and operating cloud-native solutions end to end—from data ingestion and transformation to secure service layers and intuitive dashboard experiences.

In this role, you will:

Software Solutioning & Design (10%)

• Develop and maintain a thorough understanding of the customer’s business processes and operations
• Work closely with Solutions Architect and other Lead Engineers evaluating feature requests, providing level-of-effort estimates and contributing to sprint planning
• Conduct and participate in peer code and design reviews

Design, Architecture & Implementation (60%)

• Shape the technical direction, scalability, and reliability of enterprise analytics platforms supporting mission-critical exam operations
• Design, implement, and maintain resilient cloud-native data architectures, real-time telemetry pipelines, APIs, and high-performance reporting systems
• Build secure, scalable full-stack solutions spanning backend services, data platforms, and front-end analytics applications
• Lead end-to-end solution design—from data ingestion and transformation to secure service layers and intuitive dashboard experiences
• Establish and enforce strong architectural patterns, automation standards, and observability practices
• Engineer and maintain systems with a focus on continued scalability, data integrity, high availability, and long-term reliability
• Embed monitoring, telemetry, and operational readiness into all platform components

Technical Leadership & Engineering Excellence (20%)

• Serve as a technical anchor for the team, guiding complex design decisions and large-scale problem-solving efforts
• Mentor engineers and elevate development standards across cloud, data, and full-stack domains
• Clearly communicate architectural tradeoffs, technical strategy, and platform decisions to cross-functional stakeholders
• Promote best practices in secure system design, automation, performance optimization, and resilient engineering
• Team Operations & Cross-Functional Collaboration (10%)
• Partner with product, operations, and security stakeholders to align technical solutions with business and operational priorities
• Support high-stakes exam readiness through proactive risk identification, capacity planning, and reliability reviews
• Foster a culture of ownership, documentation, accountability, and continuous improvement

About You

You have:

• 7+ years of experience designing, building, and operating scalable, cloud-native applications, data pipelines, and analytics platforms in high-availability environments
• 3+ years of experience developing modern front-end applications using TypeScript and React, with a strong focus on analytics dashboards and data-driven interfaces
• Strong hands-on experience with backend technologies such as Node.js (preferably with TypeScript) and Python, building APIs, event-driven services, and data processing components that power real-time and near–real-time analytics
• Experience designing and maintaining reliable, scalable data ingestion, transformation, and orchestration pipelines to support operational and analytical workloads
• Expertise in developing responsive, secure, and high-performance user interfaces using TypeScript, JavaScript, HTML, and CSS
• Experience implementing role-based access control (RBAC) and secure access patterns to ensure proper data governance and protection of sensitive information
• Experience with asynchronous programming, event-driven architectures, and telemetry/event-streaming patterns
• Hands-on experience with real-time data monitoring and analytics platforms such as Grafana and InfluxDB
• Strong experience with cloud-based data stores and query engines such as Amazon Redshift, Athena, DynamoDB, and S3-based data lakes, including performance optimization and trend analysis.
• Deep expertise in data modeling and transformation within AWS, leveraging services such as Glue, Redshift, Athena, EMR, Lambda, and S3 to build scalable, performant, and reliable analytical data foundations
• Experience implementing Machine Learning (ML) and Artificial Intelligence (AI) solutions within analytics platforms, including integrating predictive models, anomaly detection, trend analysis, or intelligent insights into production systems
• Familiarity with ML lifecycle practices, model deployment, monitoring, and operationalization using platforms such as SageMaker Studio, Amazon Quick Suite or similar environments
• Deep knowledge of AWS services including Lambda, SNS, SQS, S3, Step Functions, IAM, KMS, and CloudWatch
• Experience provisioning and managing cloud infrastructure using Infrastructure as Code tools such as AWS CDK, CloudFormation, Terraform, and AWS CLI
• A strong focus on scalability, data integrity, reliability, and operational readiness
• Proven ability to mentor engineers and promote engineering excellence
• Strong analytical thinking, structured problem-solving, and effective communication skills

Nice to Have:

• Exposure to building or operating analytics capabilities within a SaaS platform environment, including multi-tenant architecture considerations
• Familiarity with cell-based architecture patterns that support isolation, fault containment, horizontal scalability, and resilience at scale
• Experience designing systems that support tenant-level data isolation, performance segmentation, and secure access controls
• Understanding of platform-level observability and operational strategies in distributed, cell-based systems