Principal Data Engineer, User Success

Responsibilities

• Architect and implement scale batch and streaming pipelines for large-scale product telemetry with low-latency, high-throughput data access that support LLMs and agentic workflows optimized for: Real-time and iterative feedback loops Contextual data access Retrieval (e.g., embeddings, vector search)
• Real-time and iterative feedback loops
• Contextual data access
• Retrieval (e.g., embeddings, vector search)
• Partner with AI/ML teams to operationalize: Feature engineering and feature stores RAG-based systems and evaluation pipelines
• Feature engineering and feature stores
• RAG-based systems and evaluation pipelines
• Ensure data quality and observability meet the needs of AI-driven decision systems
• Guide build vs. buy decisions for data tooling and platforms
• Enable analysts and product teams with trusted, well-modeled datasets
• Partner with stakeholders to translate product questions into measurable data signals
• Improve instrumentation strategy to ensure high-quality behavioral data
• Support self-service analytics and AI-assisted exploration
• Collaborate across Product, Engineering, Data Science, Research and Design
• Influence technical direction without direct authority
• Drive alignment on data standards, governance, and best practices
• Communicate complex technical concepts to both technical and non-technical audiences

Minimum Qualifications

• 10+ years of experience in data engineering, data platform engineering, distributed systems, or related technical roles, including ownership of large-scale production data systems
• Strong hands-on experience with Python, Spark, PySpark, advanced SQL, and scripting
• Experience with: LLM ecosystems, embeddings, vector databases Retrieval-augmented generation (RAG) Agent frameworks or orchestration systems
• LLM ecosystems, embeddings, vector databases
• Retrieval-augmented generation (RAG)
• Agent frameworks or orchestration systems
• Experience with streaming technologies (Kafka, Flink, Spark Streaming)
• Knowledge of analytics engineering and semantic layer tools (dbt, metrics stores)
• Experience with data governance, lineage, and cataloging systems
• Exposure to product analytics and experimentation frameworks
• Experience designing and operating reliable ETL/ELT pipelines across batch and streaming workloads, including orchestration, validation, backfills, incremental processing, and data quality checks
• Experience with modern data platforms, including Iceberg, Hive, Snowflake, Redshift, Athena, or equivalent technologies
• Hands-on experience with AWS services, including EMR, Glue, S3, IAM, Lambda, Step Functions, and related cloud-native infrastructure
• Demonstrated ability to lead cross-functional technical initiatives, influence architecture, define engineering standards, and mentor engineers
• Strong communication skills with technical and non-technical stakeholders

Preferred Qualifications

• Experience with product telemetry, clickstream data, behavioral analytics, or experimentation platforms
• Experience with ingestion, orchestration, and transformation tools such as Airflow, dbt, Fivetran, or similar
• Experience partnering with product, design, research, analytics, and ML teams to create data products that directly inform user experiences or power intelligent product capabilities
• Experience supporting LLM, RAG, agentic AI, or internal intelligence workflows in production or enterprise environments
• Track record of modernizing data infrastructure in environments with fragmented systems, evolving requirements, or limited standards
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