Senior Data Integration Engineer

Job Description

Responsibilities:

• Design, build, and maintain API-based data extraction from Cisco DNA Center across multiple regions and controllers.
• Replace manual DNAC export processes with automated, scheduled, and monitored data collection.
• Normalize and structure raw network data into clean, analytics-ready datasets, including:
• Device identifiers (MAC, hostname, serial)
• SSID and connection metadata
• Connection timestamps and session duration
• Site, building, and floor mapping
• Land curated DNAC datasets into Azure Data Lake Storage (ADLS) in formats optimized for analytics.
• Validate extracted DNAC data against Intune device data and HR records identify and resolve mismatches.
• Troubleshoot missing data, duplicate records, drift in DNAC schemas, and inconsistent site metadata.
• Pipeline & Analytics-Ready Model
• Build and maintain end-to-end pipelines that ingest from Cisco DNAC, Microsoft Intune, and HR systems.
• Transform and normalize datasets across systems into a consistent presence data model.
• Define and implement join logic across systems using:
• Device ID and serial number
• Corporate email / UPN
• Employee ID where available
• Handle real-world enterprise data issues, including:
• Multiple devices per user
• Missing or partial mappings
• Inconsistent identifiers across systems
• Late-arriving and duplicated data
• Automate ingestion and refresh with reliable scheduling, monitoring, and alerting.
• Prepare analytics-ready datasets optimized for Power BI consumption (semantic clarity,performance, governance).
• Implement data quality, validation, and reconciliation checks across all sources and transformations.
• Cross-Functional Ownership
• Operate as the technical owner of the data layer end-to-end — ingest through publish.
• Partner daily / weekly with Network Engineering, Data Engineering, Workplace Analytics &
• Reporting, and HR / Intune teams
• Document extraction logic, data contracts, transformation lineage, and operational runbooks so the platform is fully supportable.

Qualification:

• Must demonstrate both halves — DNAC automation and production pipeline ownership:
• Cisco DNA Center APIs — demonstrated, hands-on production experience with Intent / Platform APIs.
• Wi-Fi client telemetry — strong understanding of clients, sessions, device tracking, and site hierarchy.
• REST APIs — authentication, pagination, rate limits and resilient extraction patterns.
• Production-level Python — pandas and modern data transformation patterns not ad-hoc scripting.
• End-to-end pipeline ownership — has personally built and operated production pipelines (ingest → transform → publish) with scheduling, monitoring, and alerting.
• Cloud storage — hands-on with Azure Data Lake Storage (ADLS) preferred AWS S3 or GCP equivalent acceptable.
• Databricks or Apache Spark for scalable transformations — strongly preferred.
• Strong SQL & data modeling fundamentals joining heterogeneous datasets.
• Comfort with structured and semi-structured data (JSON, Parquet, CSV).
• 8+ years of production data engineering with hands-on API integration 2+ years specifically with Cisco DNAC APIs and Wi-Fi telemetry.
• Microsoft Intune, CMDB, or other enterprise device datasets:
• Multi-region, multi-controller Cisco DNAC environments.
• Delta Lake, Lakehouse architectures, or medallion (bronze / silver / gold) patterns.
• Preparing datasets specifically for Power BI consumption (semantic models, performance tuning).
• Workplace analytics, occupancy, or presence-style reporting use cases.
• Version control (Git), CI/CD, and observability tooling for data pipelines.
• Cisco DevNet and/or Databricks / Azure data certifications

#26-02780