We're seeking a talented Data Platform Developer to join our Platform Engineering team and architect the data foundation that will power Newforma's next generation of AI-driven capabilities and analytics. You'll design and implement up-to-date data architectures including medallion/lakehouse patterns, build event-driven data pipelines that process billions of project documents and communications in real-time, and create the analytics infrastructure that enables both business intelligence and AI/ML initiatives. This is a foundational role at an exciting time—as we migrate to AWS and invest heavily in AI, you'll establish the data practices and infrastructure that will serve the company for years to come.

Newforma manages billions of emails, documents, RFIs, submittals, drawings, and project files for thousands of construction projects worldwide. This rich dataset represents an incredible opportunity for AI-powered insights, intelligent automation, and advanced analytics. You'll build the data infrastructure to unlock this potential, creating pipelines that transform raw project data into clean, structured, and AI-ready datasets while also enabling real-time analytics and business intelligence.

Working closely with our Director of AI Engineering and Platform Engineering team, you'll establish data architecture patterns that support everything from semantic search and RAG systems to executive dashboards and predictive analytics.

In This

Role, Your Responsibilities Will Include Data Architecture & Strategy Design and implement medallion architecture (bronze, silver, gold layers) or lakehouse patterns on AWS to organize and transform data at scale

Establish data modeling standards, governance practices, and quality frameworks across the organization

Define data retention, archival, and lifecycle management policies for massive volumes of project data

Create reference architectures and best practices for data engineering across teams

Partner with the Director of AI Engineering to design data pipelines optimized for AI/ML workloads including vector embeddings and model training

Work with the Lead Software Architect to ensure data architecture aligns with overall platform strategy

Design data schemas and structures that support both analytical queries and AI applications Event-Driven Architecture Design and implement event-driven data architectures using AWS EventBridge, Kinesis, MSK (Kafka), SNS, and SQS

Build real-time data streaming pipelines that capture, process, and route project events across the platform

Architect event schemas and patterns for domain events (document uploads, email filing, RFI submissions, etc.)

Implement change data capture (CDC) patterns to stream database changes to data lakes and analytics systems

Design event-driven workflows that trigger AI processing, notifications, and downstream system updates

Establish event governance including versioning, documentation, and monitoring





Optimize event processing for low latency and high throughput at scale Data Pipeline Development Build robust, scalable ETL/ELT pipelines using AWS Glue, Step Functions, Lambda, and EMR

Develop data transformation jobs that cleanse, enrich, and structure unstructured project data

Implement data quality checks, validation rules, and monitoring throughout pipelines

Create reusable pipeline components and frameworks that teams can leverage

Optimize pipeline performance and cost efficiency for processing billions of documents

Handle diverse data formats including emails, PDFs, CAD drawings, images, and structured databases

Implement data lineage tracking and metadata management Analytics & Business Intelligence Design and build data warehouses and data marts using Amazon Redshift, Athena, or similar technologies

Create dimensional models and star schemas optimized for analytical queries

Build datasets and aggregations that power executive dashboards and operational reports

Implement BI solutions using tools like QuickSight, Tableau, PowerBI, or similar platforms

Partner with product and business teams to understand analytics requirements and deliver insights

Create self-service analytics capabilities that empower teams to explore data independently

Establish KPIs, metrics, and reporting frameworks for product and business analytics AI/ML Data Infrastructure Prepare and structure data to support AI initiatives including document classification, semantic search, and intelligent agents

Build pipelines for generating and storing vector embeddings for RAG (Retrieval-Augmented Generation) systems

Create training datasets and feature stores for machine learning models

Implement data versioning and experiment tracking for AI/ML workflows

Design scalable inference pipelines that serve AI models with fresh, contextualized data

Collaborate with the AI Engineering team to optimize data formats and access patterns for LLM applications Data Operations & Monitoring Implement comprehensive monitoring, alerting, and observability for data pipelines and systems

Build data quality dashboards and anomaly detection systems

Create operational runbooks and documentation for data platform components

Optimize costs across data storage, processing, and querying

Ensure data security, encryption, and compliance with privacy regulations

Participate in on-call rotation to support production data systems Collaboration Collaborate with other platform engineering team members to accomplish tasks

Participate in agile ceremonies including daily stand-ups,



sprint planning, and retrospectives

Work closely with development teams and with the software architect to establish good data engineering practices for newly developed features Requirements For The Position Include 5+ years of experience in data engineering, analytics engineering, or related roles

Strong hands-on experience with AWS data services including S3, Glue, Athena, Redshift, Kinesis, EventBridge, Lambda, and EMR

Proven expertise designing and implementing event-driven architectures using streaming technologies (Kafka/MSK, Kinesis, EventBridge)

Experience building medallion architectures, lakehouse platforms, or similar modern data architectures (bronze/silver/gold patterns, Delta Lake, Iceberg)

Proficiency with SQL and database design including both relational (PostgreSQL, MySQL) and analytical databases (Redshift, Snowflake)

Strong programming skills in Python for data processing, transformation, and automation

Experience with data orchestration tools such as Apache Airflow, AWS Step Functions, or Prefect

Knowledge of data modeling techniques including dimensional modeling, star schemas, and data vault

Experience with analytics and BI tools (QuickSight, Tableau, PowerBI, Looker) and building reports/dashboards

Understanding of data quality, data governance, and master data management principles

Familiarity with infrastructure-as-code (Pulumi, Terraform, CloudFormation) for managing data infrastructure

Strong problem-solving skills and ability to optimize complex data workflows

Excellent communication skills with ability to explain technical concepts to diverse audiences

Team player who collaborates effectively across engineering, product, and business teams

Bilingual in French and English Nice To Have Qualifications For This Position Include AWS certifications (AWS Data Analytics - Specialty, AWS Solutions Architect, or similar)

Experience with Azure data services (Data Factory, Synapse, Event Hubs) and Azure-to-AWS data migrations

Knowledge of real-time stream processing frameworks (Apache Spark Streaming, Flink, Kafka Streams)

Experience preparing data for AI/ML applications including vector databases (Pinecone, Weaviate, pgvector)

Familiarity with document processing, OCR, and unstructured data extraction techniques

Experience with data catalog and metadata management tools (AWS Glue Data Catalog, Alation, Collibra)

Knowledge of .NET/C# and integrating data pipelines with .NET applications

Understanding of SaaS multi-tenancy patterns in data architecture

Experience with data privacy and compliance frameworks (GDPR, SOC 2, CCPA)

Background in the AECO industry or project management domain

Familiarity with graph databases (Neptune, Neo4j) for relationship modeling

Experience with serverless data architectures and cost optimization strategies

Knowledge of dbt (data build tool) or similar transformation frameworks

📌 Data Platform Developer (Montreal)
🏢 Newforma
📍 Montreal