Data & Analytics Engineer — AWS + Full-Stack

Fearless; United States; A distancia; USD 96.000 - 138.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

401(k) with match

Parental Leave

Paid holidays

Employee bonus program

Descripción de la vacante

Fearless is seeking a Data & Analytics Consultant to build, integrate, and support data-driven capabilities in a cloud-based enterprise environment. You will develop and maintain data pipelines, integrate data from multiple systems, and deliver reliable datasets for reporting, dashboards, and ML applications using AWS and Agile/DevSecOps practices.

You will work with engineers, product owners, business analysts, and architects to solve mission-focused data problems, improve data quality, and

Formación

• At least six (6) years of IT experience focused on development projects using DevSecOps and AWS cloud environments.
• Experience with full-stack engineering, including database development/integration and server-side and client-side application development/integration.
• At least three (3) years of experience deploying production enterprise applications in AWS.
• At least three (3) years of software engineering experience related to front-end and back-end applications and/or data services.
• Experience with large-scale, high-performance enterprise big data application deployment and solution architecture in complex, heterogeneous AWS environments.
• Experience performing automation and engineering tasks involving implementation, data, infrastructure/operations, and security in USCIS cloud environments.
• The contract requires a Public Trust clearance, which will be initiated once selected for the role.

Responsabilidades

• Build, maintain, and troubleshoot ETL/ELT pipelines that bring data together from multiple source systems.
• Develop data transformations, normalization routines, queries, and processing jobs to produce consistent, reliable datasets.
• Integrate data from APIs, application services, databases, and existing enterprise systems.
• Analyze data quality, investigate discrepancies across systems, and implement improvements to data consistency and availability.
• Develop datasets and data products that support operational metrics, program and product performance reporting, dashboards, trend analysis, and ad hoc investigations.
• Identify anomalies and unusual patterns, and measure the operational impact of application releases and system changes.
• Support entity-resolution capabilities by matching records across systems, comparing attributes, addressing inconsistent identifiers, and reducing duplicate records.
• Evaluate matching quality, including confidence levels, false matches, and missed matches; support the use of structured and, where applicable, biometric information.
• Develop or support algorithms and machine-learning approaches that improve record matching and analytical outcomes, as appropriate to project needs.
• Automate recurring data processes and support data migrations, application changes, and production troubleshooting.
• Contribute to solution design and deployment for large-scale, high-performance enterprise data applications in AWS environments.
• Apply secure engineering practices and participate in Agile ceremonies, source control, CI/CD, and federal cloud security and compliance processes.

Conocimientos

Analytical thinking

Synthetic thinking

Adaptability

Ownership & Accountability

Consultative Advising

Communicating with Influence

Environmental Awareness

Herramientas

AWS

CI/CD

Descripción del empleo

Fearless is seeking a Data & Analytics Consultant to build, integrate, and support data-driven capabilities in a cloud-based enterprise environment. You will develop and maintain data pipelines, integrate data from multiple systems, and deliver reliable datasets for reporting, dashboards, and ML applications using AWS and Agile/DevSecOps practices.

You will work with engineers, product owners, business analysts, and architects to solve mission-focused data problems, improve data quality, and