Staff Data Engineer - Core Data Pipelines

Our team at KODE Labs is looking for a Staff Data Engineer to lead the technical foundations behind our core data pipelines, data alignment, quality, lineage, and observability standards.

This is a highly cross-functional technical leadership role. You’ll build shared data foundations while defining the patterns and standards used by teams across Integrations, Digital Twin, Data Platform, Fault Detection & Diagnostics (FDD), Energy, Analytics, APIs, and AI Agents.

You won’t own every product-specific pipeline or validation rule. Instead, you’ll establish the foundations that allow teams across KODE to build on trusted, scalable, observable, and consistently modeled data.

What You Will Do

• Design, build, and evolve core data pipelines and datasets that serve as shared foundations across KODE OS.

• Define reusable patterns for aligning IoT, BMS, operational, asset, meter, work order, schedule, weather, occupancy, and time-series data with our Digital Twin.

• Establish standards for the full data lifecycle — from raw ingestion through alignment, cleaning, modeling, serving, and consumption.

• Build shared frameworks for data validation, normalization, lineage, observability, and readiness.

• Define common approaches to data quality, including quality flags, confidence scores, validation signals, and product-readiness checks.

• Establish time-series standards covering timestamps, time zones, expected frequencies, gaps, late-arriving data, duplicates, and standard aggregation grains.

• Define and evolve data contracts, schema standards, and versioning practices across data producers and consumers.

• Build observability foundations that surface issues with freshness, coverage, schema changes, data quality, pipeline failures, and data drift before they impact downstream products.

• Establish clear lineage from raw → ingested → aligned → cleaned → modeled → served → consumed data.

• Develop reusable mapping and alignment patterns that make onboarding new integrations and data sources more consistent and scalable.

• Partner closely with Integrations and Digital Twin engineers to ensure required identifiers, metadata, entity relationships, point classifications, and semantic alignment are reliable.

• Work with FDD, Energy, Analytics, API, and AI teams to ensure downstream products consume data with clear quality, freshness, lineage, semantic context, and readiness indicators.

• Review new data models, schemas, pipelines, mappings, and cross-team data flows to ensure they follow shared engineering standards.