Big Data Engineer

Job not on LinkedIn

🔥 3 hours ago

⚔️ Virginia – Remote

⚔️ Virginia – Remote

💵 $104.6k - $156.8k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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Dayforce

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

👥 HR Tech

☁️ SaaS

🏢 Enterprise

💰 $1G Post-IPO Debt - Dayforce on 2024-03

HR Tech • SaaS • Enterprise

Dayforce is a cloud-based human capital management (HCM) platform that provides payroll, workforce management, time and attendance, benefits administration, talent management, and HR tools as an integrated SaaS solution for employers. It is designed to help organizations manage payroll, scheduling, compliance, and employee lifecycle processes in a single unified application.

📋 Description

• Design and deliver scalable, secure data pipelines and data models that safely connect operational systems to analytics. • Ensure trusted and well‑governed data, enabling repeatable delivery of BI, ML, AI, and automation solutions. • Build ingestion pipelines (batch, CDC, streaming) from various sources into landing, curated, and semantic layers. • Implement data contracts, schema/versioning, SCD handling, partitioning, and performance tuning. • Develop dimensional/semantic models that back certified Power BI datasets and APIs for apps/agents. • Integrate OT data via OPC UA/MQTT and collaborate on change control and signal quality. • Embed data quality rules and instrument lineage and end-to-end monitoring; build alerting and on-call runbooks. • Automate build/test/deploy with Git-based CI/CD; track and optimize cost/performance; contribute to FinOps reviews. • Partner with Reporting & BI on semantic model contracts and produce documentation.

🎯 Requirements

• Minimum 5 years' in data engineering building production pipelines at scale (batch/CDC/streaming). • Hands-on with Azure data stack: Databricks or Fabric/Synapse, ADF/Pipelines, ADLS/OneLake, Azure SQL/SQL MI, Key Vault. • Strong SQL and Python/PySpark; comfort with Spark Structured Streaming and performance tuning. • Experience implementing tests/observability (freshness, schema, expectations), and Git-based CI/CD. • Familiarity with SAP S/4HANA structures and SAP DataSphere semantic modeling. • OT concepts: historians (PHD/PI), OPC UA/MQTT, event/batch frames, ISA-95/99 basics. • Understanding of Power BI consumption (semantic models, RLS) and APIs for downstream AI/ML apps/agents.