Senior Product Software Engineer

Jobtailor; California (MO); On-site; USD 120,000 - 170,000; Full time

Job summary

Jobtailor is seeking an experienced Data Engineer to build and optimize streaming and batch data pipelines powering AI applications. You will lead embedding workflows, vector store integrations, and scalable storage patterns across teams.

The role requires hands‑on experience with Python/Java, streaming tech, and orchestration tools, with a strong focus on data quality and governance in a production environment.

Qualifications

• 5+ years of data engineering experience, with at least 1 year in a lead or senior role.
• Experience building and scaling streaming data pipelines in large-scale distributed environments.
• Strong skills in Python, Java and SQL with expert level in either Python or Java.
• Experience with embedding pipelines and vector stores (e.g., Pinecone, Weaviate, FAISS, pgvector).
• Hands‑on experience with workflow orchestration tools (Airflow, Dagster, etc.).

Responsibilities

• Build and maintain high‑performance streaming and batch data pipelines powering AI applications.
• Implement and extend embedding generation workflows and vector store integrations.
• Develop scalable storage and retrieval patterns focusing on cost‑efficient architecture.
• Implement AI‑optimized data models and storage patterns aligned with enterprise architecture.
• Integrate pipelines with shared AI platform services with versioned data delivery.
• Build reusable ingestion, transformation, and data processing components.
• Embed end‑to‑end observability into data systems with metrics and alerts.
• Implement data quality validation, schema evolution safeguards, and governance controls.
• Drive execution by owning prototyping, implementation, testing, deployment, and documentation.
• Collaborate with infrastructure, ML engineering, product, and governance teams.
• Lead by example through high quality code and proactive problem solving.

Skills

Collaboration

Communication

Problem solving

Leadership

Execution

Tools

Python

Java

SQL

Kafka

Flink

Spark

Kinesis

Pinecone

Weaviate

FAISS

pgvector

Airflow

Dagster

Job description

Responsibilities

• Build and maintain high‑performance streaming and batch data pipelines that power AI applications, ensuring reliable low‑latency ingestion and high‑throughput processing.
• Implement and extend embedding generation workflows, vector store integrations, and retrieval pipelines supporting semantic search, RAG systems, and AI assistants.
• Develop and optimize scalable storage and retrieval patterns, focusing on cost‑efficient architecture and smooth production performance.
• Implement AI‑optimized data models and storage patterns that align with broader enterprise architecture and platform requirements.
• Integrate pipelines with shared AI platform services (agent frameworks, registries, feature stores), ensuring clean, versioned, and reliable data delivery.
• Build reusable ingestion, transformation, and data processing components that streamline adoption across engineering teams.
• Embed end‑to‑end observability into data systems, including metrics, structured logging, automated alerts, drift detection, and failure analysis.
• Implement robust data quality validation, schema evolution safeguards, and governance/compliance controls.
• Drive execution by owning the full development lifecycle: prototyping, implementation, testing, deployment, optimization, and documentation.
• Collaborate closely with infrastructure, ML engineering, product, and governance teams to deliver production‑ready AI capabilities.
• Lead by example through strong execution, high‑quality code, and proactive problem solving.

Requirements

• 5+ years of data engineering experience, with at least 1 year in a lead or senior technical role.
• Experience building and scaling streaming data pipelines in large-scale, distributed environments.
• Strong skills in Python, Java and SQL with expert level skill in either Python or Java.
• Proven experience building streaming data pipelines (e.g., Kafka, Flink, Spark, Kinesis).
• Experience with embedding pipelines and vector stores (e.g., Pinecone, Weaviate, FAISS, pgvector).
• Strong knowledge of data modeling, storage optimization, and retrieval patterns for large-scale systems.
• Hands‑on experience with workflow orchestration tools (Airflow, Dagster, etc.).
• Strong collaboration and communication skills, able to partner across AI engineering, infra, and product teams.

Hard Skills

• data engineering
• streaming data pipelines
• Python
• Java
• SQL
• data modeling
• storage optimization
• workflow orchestration
• embedding generation
• data quality validation

Soft Skills

• collaboration
• communication
• problem solving
• leadership
• execution