Staff Data Engineer

Katalyze AI, Inc.; Toronto; On-site; CAD 120,000 - 180,000; Full time

Job summary

Katalyze AI is seeking a Staff or Senior Data Engineer to own the data infrastructure powering its AI-driven platform. You will define standards, build real-time data streams, and architect pipelines that connect customer systems to ensure clean, reliable data for model training and inference.

You will collaborate with ML and Data Science teams to deliver well-structured data and design data schemas optimized for AI/ML workloads, while engaging with customer IT to ensure secure connections and

Qualifications

• 7+ years of data engineering experience, owning systems — not just building within them.
• Experience making architecture decisions: choosing tools, designing schemas, defining standards.
• Deep expertise in data streaming (Kafka, Kinesis, Flink, or Spark Streaming) — production, scalable.
• Strong proficiency in building data integrations with external enterprise systems (REST APIs, OPC-UA).
• Experience with cloud data platforms (AWS Glue, Databricks, Snowflake, or similar).
• Proficiency in Python and SQL; experience with dbt or similar transformation tooling.
• Background in industrial data systems (OSIsoft PI, Ignition, MES/LIMS integrations) is a strong plus.
• Comfortable communicating technical tradeoffs to non-technical stakeholders.

Responsibilities

• Own the data infrastructure architecture — define standards, patterns, and tooling decisions for the data layer.
• Design and build data integration pipelines connecting customer systems (MES, LIMS, ERP, historians) to the platform.
• Develop and operate real-time and batch data streaming infrastructure at scale.
• Build and maintain ETL/ELT pipelines for structured and unstructured scientific data.
• Establish data quality, reliability, and observability frameworks across all pipelines.
• Collaborate with ML and Data Science teams to deliver clean, well-structured data for model training and inference.
• Design data schemas and storage solutions optimized for AI/ML workloads.
• Work with customer IT teams during deployments to establish secure data connections and meet compliance.

Skills

Data architecture

Data streaming

Python

SQL

Data integration

Observability

Stakeholder communication

Tools

Kafka

Kinesis

Flink

Spark Streaming

dbt

REST APIs

OPC-UA

Databricks

Snowflake

AWS Glue

Job description

About Katalyze AI

Katalyze AI is a fast-growing AI-driven biotech platform company on a mission to make life-saving drugs accessible and affordable for everyone. Our AI Agents help pharmaceutical and biotech companies increase production efficiency, reduce costs, and minimize waste. We're a team of humble, fast-moving, and curious craftspeople working at the intersection of science and AI.

About the Role

We're looking for a Staff or Senior Data Engineer to own the data infrastructure that powers Katalyze AI's platform. You'll make architecture decisions, design integrations with customer data systems, and build the streaming pipelines that give our AI models and agents access to clean, reliable, real-time data — setting the patterns the team builds on as we scale.

What You'll Do

Own the data infrastructure architecture — define standards, patterns, and tooling decisions for the data layer

Design and build data integration pipelines connecting customer systems (MES, LIMS, ERP, historians) to the Katalyze AI platform

Develop and operate real-time and batch data streaming infrastructure (Kafka, Kinesis, or similar) at scale

Build and maintain ETL/ELT pipelines for structured and unstructured scientific data

Establish data quality, reliability, and observability frameworks across all pipelines

Collaborate with ML and Data Science teams to deliver clean, well-structured data for model training and inference

Design data schemas and storage solutions (data lakes, warehouses) optimized for AI/ML workloads

Work directly with customer IT teams during deployments to establish secure data connections and meet compliance requirements

Set technical direction for the data engineering function as the team grows

What We're Looking For

7+ years of data engineering experience, with a track record of owning systems — not just building within them

Demonstrated experience making architecture decisions: choosing tools, designing schemas, defining standards

Deep expertise in data streaming (Kafka, Kinesis, Flink, or Spark Streaming) — designed and operated in production, at scale

Strong proficiency in building data integrations with external enterprise systems (REST APIs, OPC-UA, proprietary connectors)

Experience with cloud data platforms (AWS Glue, Databricks, Snowflake, or similar)

Proficiency in Python and SQL; experience with dbt or similar transformation tooling

Strong data quality and observability instincts — you've built frameworks, not just used existing tools

Background in industrial data systems (OSIsoft PI, Ignition, MES/LIMS integrations) is a strong plus

Comfortable communicating technical tradeoffs to non-technical stakeholders