Technical Product Owner- Data Integration - W2 Only (NO C2C / C2H)
Posted Today by Symphony Corporation
Negotiable
Undetermined
Remote
Remote
Summary: The Technical Product Owner for Data Integration is responsible for managing the product backlog, ensuring the development of scalable and reusable enterprise data platform capabilities. This role requires collaboration with engineering teams to define data ingestion patterns and drive Agile ceremonies while incorporating AI automation into workflows. The position demands hands-on technical skills to create proof-of-concepts and guide the development team effectively. The role is fully remote and requires a deep understanding of both business intent and technical implementation.
Key Responsibilities:
- Own the product backlog: decompose epics and business vision into well-defined, developer-ready features and user stories
- Partner with engineering and architecture teams to design scalable, reusable enterprise data platform capabilities
- Define and govern batch and streaming data ingestion and integration patterns across the platform
- Drive and own ceremonies of SAFe Agile / LPM, including PI planning, iteration goals, and backlog refinement
- Incorporate agentic AI and LLM-based automation into platform development workflows
- Align cross-functional stakeholders on technical tradeoffs, platform roadmap, and delivery priorities
- Ensure platform capabilities are cloud-native, scalable, and built for reuse across enterprise consumers
- Must be hands-on technical with the ability to independently write proof-of-concepts (POCs) and reference implementations to guide and accelerate the development team
- Build reusable data integration and data ingestion platform capabilities that can be leveraged by all application teams to acquire data from systems of record and make it available in the data platform
- Build capabilities to catalog acquired data from systems of record, enabling discoverability and governance across the platform
- Expected to deeply understand the "what" — the business intent and product vision — and independently derive a very detailed and technical "how" — the implementation approach, architecture decisions, and engineering specifications ready for the development team
Key Skills:
- Data Processing: Python, PySpark, PyFlink, ETL/ELT batch & streaming patterns
- Data Platforms: Databricks, Databricks Lakeflow, structured & unstructured data engineering
- Databases & CDC: MS SQL Server, PostgreSQL, MariaDB, MongoDB (CDC patterns), Kafka Connect, Debezium
- Streaming & Eventing: Confluent Kafka, AWS EventBridge, AWS Kinesis, AWS SQS, Google Pub/Sub, Azure Event Hub
- Application Development: REST APIs, Python-based service development
- Cloud & Infrastructure: Azure, Google Cloud Platform, Terraform, Kubernetes
- Agile: SAFe / LPM — PI planning, epic decomposition, backlog ownership
- AI/ML: Agentic AI development patterns and integration workflows
Salary (Rate): undetermined
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Technical Product Owner- Data Integration:
W2 Only (NO C2C / C2H)
Remote Only
• Own the product backlog: decompose epics and business vision into well-defined, developer-ready features and user stories
• Partner with engineering and architecture teams to design scalable, reusable enterprise data platform capabilities
• Define and govern batch and streaming data ingestion and integration patterns across the platform
• Drive and own ceremonies of SAFe Agile / LPM, including PI planning, iteration goals, and backlog refinement
• Incorporate agentic AI and LLM-based automation into platform development workflows
• Align cross-functional stakeholders on technical tradeoffs, platform roadmap, and delivery priorities
• Ensure platform capabilities are cloud-native, scalable, and built for reuse across enterprise consumers
• Must be hands-on technical with the ability to independently write proof-of-concepts (POCs) and reference implementations to guide and accelerate the development team
• Build reusable data integration and data ingestion platform capabilities that can be leveraged by all application teams to acquire data from systems of record and make it available in the data platform
• Build capabilities to catalog acquired data from systems of record, enabling discoverability and governance across the platform
• Expected to deeply understand the "what" — the business intent and product vision — and independently derive a very detailed and technical "how" — the implementation approach, architecture decisions, and engineering specifications ready for the development team
Technical Skills Required
Area Skills
Data Processing Python, PySpark, PyFlink, ETL/ELT batch & streaming patterns
Data Platforms Databricks, Databricks Lakeflow, structured & unstructured data engineering
Databases & CDC MS SQL Server, PostgreSQL, MariaDB, MongoDB (CDC patterns), Kafka Connect, Debezium
Streaming & Eventing Confluent Kafka, AWS EventBridge, AWS Kinesis, AWS SQS, Google Pub/Sub, Azure Event Hub
Application Development REST APIs, Python-based service development
Cloud & Infrastructure Azure, Google Cloud Platform, Terraform, Kubernetes
Agile SAFe / LPM — PI planning, epic decomposition, backlog ownership
AI/ML Agentic AI development patterns and integration workflows