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:

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.

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.

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

#AditiConsulting
#26-02780

Senior Data Integration Engineer