Production Data Systems & Pipelines
• Design and implement end-to-end data flows, from raw event ingestion through
durable storage and modeled datasets that power products, Digital Twin
experiences, and AI agents.
• Build reliable, incremental pipelines that support deduplication, late-arriving data,
watermarking, reprocessing, and reproducible aggregations at scale.
• Model context and relationships across machines, lines, factories, sensors, work
orders, and tenants to support structured queries and AI-driven experiences.
• Partner with platform and AI teams to define how datasets are stored, modeled, and
exposed through APIs, Digital Twin services, and context graphs.
Software Engineering & Quality
• Build clean, maintainable Python services with strong separation of concerns
across validation, persistence, aggregation, and orchestration layers.
• Apply strong SQL and data modeling practices, including schema design, indexing,
constraints, timestamp semantics, and scalable aggregations.
• Drive engineering quality through automated testing, including unit, integration, and
data-focused validation for correctness and reliability.
• Design for observability through metrics, logging, and tracing that support
debugging, data quality monitoring, production incidents, and backfills.
Streaming, Lakehouse & Scalability
• Design and evolve streaming-first architectures using lakehouse and messaging
technologies, including partitioning, watermarking, replay, reprocessing, and costaware scaling.
• Work with technologies such as Kafka, Pub/Sub, or similar systems to build reliable
event-driven services and data pipelines.
• Contribute to multi-tenant architectures and data contracts that enable secure,
scalable access to data across products, applications, and AI agents.
Collaboration & AI-Native Experiences
• Partner closely with DIH, Smart Canvas, AI, and Product teams to design scalable
data models, APIs, and context services that power AI-native experiences.
• Translate business and product requirements into technical solutions that balance
correctness, performance, cost, and long-term maintainability.
• Participate in design reviews, code reviews, and technical discussions that raise the
engineering bar across the organization.
• Collaborate effectively across distributed teams through clear written and verbal
communication.
What You Bring
• Bachelor's degree in Computer Science, Computer Engineering, Information
Technology, or a related field. Advanced degrees or equivalent practical experience
are also valued.
• 4+ years of professional software development experience building backend
platforms, distributed systems, or data-intensive applications in production
environments.
• Strong software engineering experience in Python, SQL, and data modeling, with a
track record of building production-grade data systems and reliable, incremental
pipelines.
• Experience designing systems that handle duplicate, invalid, and late-arriving
events while maintaining correctness and reliability for downstream consumers.
• Experience with at least one cloud platform (AWS, Azure, or GCP) and modern data
technologies such as Databricks, Delta Lake, Spark, BigQuery, or similar lakehouse
architectures.
• Experience with streaming or messaging systems such as Kafka, Pub/Sub, NSQ, or
similar event-driven technologies.
• Strong operational and debugging skills, including observability, monitoring,
backfills, schema evolution, and production incident response.
• Strong written and verbal communication skills, with experience collaborating
across globally distributed teams
