Senior Data Engineer

Senior Data Engineer

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About the Role

Senior Data Engineer

construction-focused enterprise tools

You will architect scalable systems, build automated ETL/ELT workflows, standardize data governance, and enable real-time insights, predictive analytics, and automated operational intelligence across the organization.

strong technical depth, system architecture capability, and hands-on experience

Key Responsibilities

Data Architecture & System Design

• Architect

  modular, scalable, and secure

  data platforms.
• Design centralized data models to support

  analytics, forecasting, AI, and automation workloads

  .
• Define data governance standards including lineage, quality, and retention.
• Produce HLD/LLD architecture diagrams, integration maps, and technical documentation.
• Build frameworks that support complex cross-system workflows.

Integrations Across Enterprise Tools (Sage 300, Paperless, WorkMax, Procore, Kojo, CRM, etc.)

• Lead integrations across construction and operations software including:

• Sage 300

  (Financials, Job Costing, AP/AR, GL)

• Paperless

  (Document workflows, invoice automation)

• WorkMax

  (Timecards, units, production, field data)

• Procore

  (Project management, RFIs, submittals, schedules)

• Kojo

  (Procurement, materials management)

• CRM systems

  (Sales pipeline, customer records)
• Develop connectors using

  REST APIs, GraphQL, Webhooks, Secure File Transfer, and Database links

  .
• Standardize schemas across tools to create a

  single source of truth

  .
• Ensure secure and reliable data ingestion from on-prem and cloud systems.
• Implement robust incremental sync, batch loads, and real-time event-based ingestion.

ETL/ELT Development & Data Pipelines

• Build automated ETL/ELT workflows using

  Airflow, NiFi, Azure Data Factory, or custom frameworks

  .
• Ensure pipelines are

  fault-tolerant, versioned, and optimized for performance

  .
• Handle complex transformations, reconciliations, and multi-system data validation.
• Develop end-to-end data ingestion frameworks supporting large structured and unstructured datasets.
• Deploy pipelines into production with CI/CD practices.

Data Warehousing, Modeling & Real-Time Reporting

• Build enterprise-wide data warehouse layers using

  Snowflake (preferred)

  , Azure SQL, or cloud data stores.
• Maintain

  real-time operational data models

  for job cost, purchasing, productivity, CRM, safety, compliance, and field activity.
• Create unified analytical datasets for dashboards and business intelligence.
• Support executive and departmental reporting by eliminating manual spreadsheets and fragmented data silos.
• Ensure high data accuracy, reliability, and performance at scale.

Predictive Analytics & AI Enablement

• Prepare AI-ready datasets for forecasting, anomaly detection, productivity modeling, and operational risk scoring.
• Collaborate with data scientists/AI engineers to build and deploy ML pipelines.
• Develop streaming/near-real-time data flows to support AI-driven decision automation.
• Integrate predictive insights back into operation systems (alerts, workflows, dashboards).

Automation & Operational Workflows

• Automate KPI tracking, threshold-based alerts, and data validation rules.
• Build automated workflows for:
• RFIs
• CORs
• Contracts
• Vendor documents
• Field reporting
• Implement monitoring frameworks for pipeline health, error handling, retry logic, and SLA compliance.

Documentation & Knowledge Sharing

• Create detailed documentation for data models, transformations, integrations, and pipelines.
• Conduct internal training and handover sessions to engineering, analytics, and operations teams.
• Maintain continuous improvement of data standards and reusable components.

Skills & Experience Required

Core Experience

• 3–5 years of hands-on

  Data Engineering / Data Platform development

  .
• Strong expertise in

  API-based integrations

  , especially with enterprise applications.
• Practical experience with:

• Python

  , FastAPI, Node.js
• ETL frameworks:

  Airflow / NiFi / Azure Data Factory

• Snowflake

  (strongly preferred) or Azure-based data warehousing
• SQL + cloud databases
• Azure Functions, Storage, Data Lake environments

Data Architecture & Modeling

• Strong data modeling principles (Kimball, Data Vault, or custom patterns).
• Building scalable data lakes, warehouse schemas, and analytical data marts.

Tools & Platforms

• Dashboarding tools:

  Power BI / Tableau

• Experience working with large structured, semi-structured, and unstructured datasets

  .
• Background in financial or field operations workflows (preferred).

Preferred Knowledge

• Working with industry tools such as:

• Sage 300

• Procore

• WorkMax

• Kojo

• Paperless

• Experience in building ML/AI data pipelines.
• Exposure to

  Azure DevOps, CI/CD

  , and cloud deployment best practices.

Key Competencies

• Strong system design and architecture thinking.
• High ownership, accountability, and delivery focus.
• Excellent analytical and problem-solving abilities.
• Clear communication and documentation skills.
• Ability to work with leadership, operations, finance, field teams, and engineering.
• Commitment to data accuracy, reliability, and operational performance.

What We Offer

• Opportunity to build a

  next-generation Enterprise AI Data Platform

  .
• Career growth into

  Data Platform Lead / Engineering Manager roles

  .
• Medical insurance and employee benefits.
• Supportive leadership, high visibility, and impactful responsibilities.
• Competitive compensation aligned with expertise and contribution.