Senior Data Integration Engineer

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 Dev

Net and/or Databricks / Azure data certifications

#Aditi

Consulting
#26-02780