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Staff Data Engineer - Core Data Pipelines

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Apply at Kodelabs

What you’ll work on

Full posting
  • 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.

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

From the employer’s posting
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.
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.
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.

What you’ll bring

All qualifications

Core experience

  • Strong experience building and operating core data pipelines, including batch and/or streaming architectures.
  • Deep understanding of data modeling, data quality, contracts, lineage, observability, and schema evolution.
  • Strong proficiency in SQL and Python.
  • Experience working with IoT, telemetry, time-series, operational, or other high-volume event data.
  • Experience with Digital Twins, ontologies, semantic models, metadata modeling, or domain-driven data structures.
  • Hands-on experience with technologies such as Kafka, Flink, Spark, Airflow, dbt, BigQuery, ClickHouse, PostgreSQL, or comparable tools.
Qualification wording
Strong experience building and operating core data pipelines, including batch and/or streaming architectures.
Deep understanding of data modeling, data quality, contracts, lineage, observability, and schema evolution.
Strong proficiency in SQL and Python.
Experience working with IoT, telemetry, time-series, operational, or other high-volume event data.
Experience with Digital Twins, ontologies, semantic models, metadata modeling, or domain-driven data structures.
Hands-on experience with technologies such as Kafka, Flink, Spark, Airflow, dbt, BigQuery, ClickHouse, PostgreSQL, or comparable tools.

Tools in this posting

  • SQL
  • BigQuery
  • ClickHouse
  • dbt
  • Kafka
  • PostgreSQL
  • Spark
  • Airflow
  • Python
Source — Tool mentions in context
- Deep understanding of data modeling, data quality, contracts, lineage, observability, and schema evolution. - Strong proficiency in SQL and Python. - Experience working with IoT, telemetry, time-series, operational, or other high-volume event data.
- Experience with Digital Twins, ontologies, semantic models, metadata modeling, or domain-driven data structures. - Hands-on experience with technologies such as Kafka, Flink, Spark, Airflow, dbt, BigQuery, ClickHouse, PostgreSQL, or comparable tools. - Strong systems thinking, with the ability to design data flows from ingestion and alignment through modeling and downstream consumption.

Benefits in the posting

Full benefits wording
  • Competitive salary based on experience
  • Discretionary Bonus Program
  • Career Development Program and opportunities to grow within the company
  • Flexible Paid Time Off
  • Comprehensive Health Insurance Package
  • Dynamic team and challenging projects
  • Custom-tailored onboarding experience
  • Welcoming and friendly work environment
  • Social events and team activities
  • JOIN THE TEAM
  • KODE Labs is a real estate technology company founded in 2017 with a mission to change the way people, buildings, and systems operate. Headquartered in Detroit, Michigan, we are a driving force behind the adoption of smart building technology. To scale our presence across numerous cities and countries, we depend on our team of talented, ambitious people who go above and beyond to create value for our clients.
  • If KODE Labs sounds like the place for you, we’d love to hear your story, so please apply through our online application form. Only shortlisted candidates will be contacted.

From the employer’s posting.

Posting record

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First seen by us
Aug 23, 2026 · 54d ago
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Last seen by us
Oct 15, 2026

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Job description

View original posting ↗

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.

  • Provide technical leadership across teams, helping engineers make strong architectural decisions and avoid solving the same data problems in different ways.

  • Mentor engineers and raise the bar for data engineering practices, system design, documentation, reliability, and maintainability across KODE.



Requirements

  • Staff-level experience in data engineering or data platform engineering, with a track record of designing scalable systems and leading technical initiatives across teams.
  • Strong experience building and operating core data pipelines, including batch and/or streaming architectures.

  • Deep understanding of data modeling, data quality, contracts, lineage, observability, and schema evolution.

  • Strong proficiency in SQL and Python.

  • Experience working with IoT, telemetry, time-series, operational, or other high-volume event data.

  • Experience with Digital Twins, ontologies, semantic models, metadata modeling, or domain-driven data structures.

  • Hands-on experience with technologies such as Kafka, Flink, Spark, Airflow, dbt, BigQuery, ClickHouse, PostgreSQL, or comparable tools.

  • Strong systems thinking, with the ability to design data flows from ingestion and alignment through modeling and downstream consumption.

  • Proven ability to set technical standards, influence architecture across teams, and mentor engineers without directly owning every implementation.



Benefits

WHAT WE OFFER:

  • Competitive salary based on experience 

  • Discretionary Bonus Program 

  • Career Development Program and opportunities to grow within the company 

  • Flexible Paid Time Off

  • Comprehensive Health Insurance Package 

  • Dynamic team and challenging projects 

  • Custom-tailored onboarding experience 

  • Welcoming and friendly work environment 

  • Social events and team activities


JOIN THE TEAM

KODE Labs is a real estate technology company founded in 2017 with a mission to change the way people, buildings, and systems operate. Headquartered in Detroit, Michigan, we are a driving force behind the adoption of smart building technology. To scale our presence across numerous cities and countries, we depend on our team of talented, ambitious people who go above and beyond to create value for our clients.

When you join the KODE Labs team you can create your own career. Whether you have years of experience or are just starting, we help you realize your full potential and achieve your goals.

If KODE Labs sounds like the place for you, we’d love to hear your story, so please apply through our online application form. Only shortlisted candidates will be contacted.



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  • Check the listed location, eligibility and core experience before starting.
  • Ask the employer about the salary range before committing time to the process.

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