Data Engineer

AARP’s Data & Analytics team uses data engineering and analytics to improve marketing and communications results and support data-driven decisions. The Data Engineer translates business requirements into technical specifications, delivers and supports technical solutions, and works with cross-functional teams to improve technology processes.

Responsibilities

• Establishes a technical roadmap for the platform and/or capability strategy and lifecycle that considers value-based outcomes, costs to maintain, supportability, and performance
• Ensures sound integration, data, security, and business architecture design throughout all stages within the platform and/or capability lifecycle
• Provides rapid delivery and development of technical solutions that align with business and/or platform desired outcomes
• Troubleshoots and resolves technical issues related to platform or capability systems, solutions, and services
• Innovates and drives continuous improvements of implementation methodology and technical service offerings based on customer/employee experiences or other enterprise objectives/outcomes
• Participates in a Community of Interest for engineers across all capability and platform teams to share information and strengthen understanding of business needs and technology-based business solutions
• Develops and maintains deep technical knowledge and expertise related to domain area systems, solutions, services, and applications

Preferred

• Experience implementing data quality frameworks, validation rules, reconciliation processes, automated testing, monitoring, observability, and alerting to detect and remediate pipeline failures, schema changes, and data quality anomalies
• Knowledge of data integrity, lineage, metadata management, and data governance, including root cause analysis, with familiarity in platforms such as Collibra, Unity Catalog, Great Expectations, Monte Carlo, Informatica Data Quality, or similar technologies preferred
• Experience developing AI-powered analytics, conversational data applications, or intelligent agents using Databricks Genie, Databricks AI/BI, large language models, or similar technologies, with familiarity in multi-agent architecture, persona-based simulation, prompt engineering, retrieval-augmented generation, and governed grounding
• Experience preparing data products, semantic models, metadata, and business definitions for AI agents and developing evaluation frameworks that assess AI-generated responses for accuracy, relevance, consistency, grounding, bias, and safety
• Data Quality Frameworks
• Data Validation
• Data Reconciliation
• Automated Testing
• Data Monitoring
• Data Observability
• Alerting
• Pipeline Failure Remediation
• Schema Change Detection
• Data Anomaly Detection
• Data Integrity
• Data Lineage
• Metadata Management
• Data Governance
• Root Cause Analysis
• Collibra
• Unity Catalog
• Great Expectations
• Monte Carlo
• Informatica Data Quality
• AI-Powered Analytics
• Conversational Data Applications
• Intelligent Agent Development
• Databricks Genie
• Databricks AI/BI
• Large Language Models
• Multi-Agent Architecture
• Persona-Based Simulation
• Prompt Engineering
• Retrieval-Augmented Generation
• Governed Grounding
• Data Product Preparation
• Semantic Modeling
• Business Definitions
• AI-Ready Metadata
• AI Evaluation Frameworks
• Response Accuracy Evaluation
• Relevance Evaluation
• Consistency Evaluation
• Grounding Evaluation
• Bias Evaluation
• AI Safety Evaluation

Benefits

• 401(k)
• 100% company-funded pension plan
• Health, dental, and vision plans
• Life insurance
• Paid time off, including company and individual holidays, vacation, sick, caregiving, and parental leave
• Performance-based and peer-based recognition
• Tuition reimbursement
• Mondays and Fridays as remote workdays, except for essential functions; remote work can only be done within the United States and its territories

Company

• Nonprofit advocacy and services organization for Americans 50-plus
• Washington, District of Columbia, USA
• http://www.aarp.org/
• Founded in 1958
• 1001-5000 employees

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or equivalent experience, plus 3+ years of hands-on experience designing, building, and maintaining data pipelines and large-scale data infrastructure in a technical business environment
• Strong proficiency in SQL and Python, including Databricks, PySpark, or similar frameworks, and experience with Big Data and distributed processing technologies such as Spark, Hadoop, and Delta Lake
• Experience with data integration, including ETL/ELT, APIs, streaming, and batch processing; structured and unstructured datasets; and platforms such as Databricks, AWS, Informatica IICS, or similar frameworks
• AARP will not sponsor an employment visa for this position
• Effective verbal and written communication skills
• Independent judgement in evaluation options to make sound decisions
• Experience implementing data quality frameworks, validation rules, reconciliation processes, automated testing, monitoring, observability, and alerting to detect and remediate pipeline failures, schema changes, and data quality anomalies
• Knowledge of data integrity, lineage, metadata management, and data governance, including root cause analysis, with familiarity in platforms such as Collibra, Unity Catalog, Great Expectations, Monte Carlo, Informatica Data Quality, or similar technologies preferred
• Experience developing AI-powered analytics, conversational data applications, or intelligent agents using Databricks Genie, Databricks AI/BI, large language models, or similar technologies, with familiarity in multi-agent architecture, persona-based simulation, prompt engineering, retrieval-augmented generation, and governed grounding
• Experience preparing data products, semantic models, metadata, and business definitions for AI agents and developing evaluation frameworks that assess AI-generated responses for accuracy, relevance, consistency, grounding, bias, and safety
• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or equivalent experience, plus 3+ years of hands-on experience designing, building, and maintaining data pipelines and large-scale data infrastructure in a technical business environment.

Required skills

• API Integration, AWS, Apache Spark, Data Pipeline Engineering, Databricks, Delta Lake, ETL/ELT, Hadoop, PySpark, Python, SQL, Streaming and Batch Processing

Preferred skills

• Verbal and Written Communication

Experience

• 3+ years