Data Engineer

PBK Architects•Los Angeles, CA

About The Position

The Data Engineer designs, builds, and supports the data pipelines, integrations, and curated datasets that power analytics, reporting, and AI initiatives for PBK’s Architecture vertical and broader AEC operations. This role owns integrations across enterprise platforms, including Salesforce, Deltek Vantagepoint, Autodesk data sources, Azure, and Microsoft Fabric. The Data Engineer ensures data is reliable, governed, secure, and available to support business visibility, project delivery, automation, and decision-making.

Requirements

5+ years of experience designing, building, and operating production data pipelines and integrations.
Strong SQL skills and experience with PostgreSQL or similar relational databases.
Proficiency in Python for data engineering, automation, and workflow development.
Hands-on experience with Microsoft Azure data services and Microsoft Fabric, or equivalent cloud data platforms.
Experience with ETL/ELT pipelines, data modeling, curated datasets, orchestration, and production data workflows.
Strong experience with APIs and integration patterns, including REST, webhooks, authentication, error handling, retry strategies, and cross-system data movement.
Familiarity with SDLC and CI/CD for data workflows using GitHub Enterprise, Azure DevOps, or similar tools.
Strong understanding of security, data governance, access controls, and secure handling of sensitive business information.
Ability to communicate clearly with technical and non-technical stakeholders in an AEC or similar business environment.
Operational discipline with the ability to maintain, monitor, troubleshoot, and improve mission-critical data flows over time.

Nice To Haves

Experience integrating enterprise platforms such as Salesforce, Deltek Vantagepoint, Autodesk Forma, Autodesk data management services, Microsoft 365, or similar systems.
Experience with Azure Logic Apps, Azure Functions, MuleSoft, Databricks, Snowflake, or other iPaaS/middleware and cloud data platforms.
Experience with dbt, Airflow, Databricks Workflows/Jobs, Fabric notebooks, Spark, or similar transformation and orchestration frameworks.
Experience with streaming or event-driven integrations using Event Grid, Service Bus, Kafka concepts, or similar technologies.
Experience with Power BI semantic models, performance optimization, and BI-ready dataset design.
Experience supporting AI/ML pipelines, RAG-oriented datasets, or data products used in AI-enabled workflows.
Strong integration owner mindset with the ability to build solutions that are maintainable, observable, secure, and supportable.
Business-oriented approach with a focus on data outcomes that improve delivery, visibility, automation, and decision-making.

Responsibilities

Design, build, operate, and support ETL/ELT pipelines and integrations across enterprise systems, including Salesforce, Deltek Vantagepoint, Autodesk data sources, Azure, and Microsoft Fabric.
Implement data models, curated datasets, Lakehouse/Warehouse structures, and transformation layers to support BI, reporting, AI, and machine learning use cases.
Build and support API-first data services, data contracts, and integration workflows using REST, JSON, SOAP where applicable, webhooks, and related patterns.
Develop orchestration and scheduling processes using Microsoft Fabric Data Pipelines, Azure Data Factory, Azure Logic Apps, Functions, Spark notebooks, dbt, or similar tools.
Apply event-driven integration patterns using Event Grid, Service Bus, Kafka concepts, or similar technologies where appropriate.
Own the operational reliability of pipelines and integrations, including monitoring, alerting, incident response, troubleshooting, root-cause analysis, and remediation.
Implement data quality checks, validation, lineage, documentation, and governance practices to ensure data is accurate, trusted, and usable.
Partner with analytics, application, IT/Security, and business teams to support secure access, data governance, compliance, and reliable data delivery.
Support AI initiatives by preparing reliable datasets, including AI/ML pipelines or RAG-oriented datasets where applicable.
Use AI tools responsibly to improve productivity, accelerate development, support testing, and improve documentation while maintaining security and quality standards.