Senior Data Engineer - Customer AI Analytics
$117,610 - $153,146 per year
ApplySenior Data Engineer - Customer AI Analytics
Posted today
SENIORITY
Senior
SALARY
$117,610 - $153,146 per year
About the role
- Transform raw conversational data, AI agent logs, and customer interaction events into structured, reliable analytics data assets
- Design and build scalable data models that support AI performance monitoring, customer journey analytics, and business impact measurement
- Create reusable data products that enable self-service analytics for business stakeholders, data scientists, and AI engineers
- Pipeline Development & Optimization:
- Build highly optimized, modular PySpark pipelines within Databricks to process large-scale conversational data and AI telemetry
- Convert ad-hoc analytical queries into production-ready data pipelines with rigorous testing and monitoring
- Implement incremental processing patterns and Delta Lake optimization techniques to minimize compute costs and improve query performance
- Conversational Data Processing:
- Parse and structure complex, semi-structured conversational data including chat transcripts, voice call logs, AI agent decision traces, and customer intent classifications
- Standardize ingestion of diverse data sources including LLM prompt/response pairs, agent orchestration logs, and customer feedback signals
- Build precise conversation funnel metrics, AI containment rates, resolution accuracy, and customer satisfaction analyticsAI Performance Analytics:
- Design data models that enable comprehensive AI agent performance monitoring including response accuracy, latency, escalation patterns, and customer satisfaction
- Create analytics frameworks that measure business impact of AI automation including cost savings, wait time reduction, and operational efficiency gains
- Build attribution logic that connects AI interactions to downstream business outcomes such as bookings, customer retention, and contact center volume reduction
- Data Governance & Quality:
- Enforce rigorous technical standards across all data products including schema enforcement, data quality validation, and comprehensive metadata documentation
- Implement standardized naming conventions, data lineage tracking, and documentation practices
- Ensure data products meet security, privacy, and compliance requirements for customer interaction data
- Visualization & Reporting:
- Expose Databricks Delta tables to PowerBI and other visualization tools for business stakeholder consumption
- Partner with business analysts and product managers to design intuitive dashboards and reports that drive decision-making
- Create automated reporting frameworks that deliver regular insights on AI performance and business impact
- Production Excellence:
- Transition experimental analytics work into robust, production-ready data assets with comprehensive monitoring and alerting
- Partner with analytics teams, AI engineers, and business stakeholders to ensure data products meet evolving business needs
- Document data products thoroughly to enable handoff to broader engineering teams for long-term maintenance
- What's needed to succeed (Minimum Qualifications):Bachelor's degree
- Computer Science, Data Engineering, Information Systems, or related field preferred 3+ years of hands-on experience designing, optimizing, and maintaining large-scale data processing workloads
- Expert-level proficiency in PySpark and Databricks (DataFrames, Structured Streaming, Delta Lake)
- Advanced SQL skills with deep expertise in writing and optimizing complex, high-volume queries
- Experience with cloud data platforms, preferably AWS (Redshift, S3, Glue, Athena)
- Proven track record of parsing and structuring complex, semi-structured data (JSON, nested structures, logs)
- Strong understanding of analytics data modeling and dimensional design principles
- Experience building data pipelines that support business intelligence and reporting use cases
- Proficiency with PowerBI or similar visualization tools for exposing data products to business users
- Self-starter mentality capable of auditing unfamiliar schemas, reverse-engineering logic, and delivering production-ready solutions with minimal guidance
- Must be legally authorized to work in the United States for any employer without sponsorship
- Successful completion of interview required to meet job qualification
- Reliable, punctual attendance is an essential function of the position
- Master's degree in Computer Science, Data Science, or related field
- Experience processing conversational data, chat logs, voice transcripts, or customer interaction data
- Familiarity with AI/ML telemetry, LLM prompt/response data, or agent orchestration logs
- Experience building analytics for AI systems including performance monitoring, accuracy measurement, and impact analysis
- Knowledge of natural language processing (NLP) concepts and text analytics
- Experience with real-time streaming data processing and event-driven architectures
- Familiarity with data quality frameworks and automated testing for data pipelines
- Experience with contact center analytics, customer journey analytics, or operational efficiency metrics
- Understanding of how to map system performance data to business outcomes and customer experience metrics
- Prior experience in travel, e-commerce, retail, or customer service industries
- Experience working in fast-paced, enterprise-scale environments with complex data ecosystems
- Knowledge of data governance, privacy regulations, and responsible AI data practices
- Familiarity with Agile/Scrum methodologies and collaborative development practices
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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