Syndicated Service Specialist

• Design, model, and build analytical datasets using Azure Databricks, and Spark-based distributed processing.
• Develop basic ingestion and transformation pipelines (batch and near-real-time) using modern data-stack practices.
• Create data-quality, lineage, and observability frameworks that promote trust, transparency, and reliability across all data layers.

• Build analytical views, dimensional models, and feature sets that support ML workflows, predictive analytics, and anomaly detection.
• Design KPI and metric layers that translate complex network telemetry into clear, consumable data products.
• Optimize large-scale datasets for performance, incremental refresh, and multi-audience consumption (curated, semantic, ML-ready).

• Analyze high-volume telemetry across Wi-Fi, DOCSIS, PON, LTE, TR-069/TR-181, and IP service platforms to surface degradation patterns and service-impacting anomalies.
• Apply statistical and correlation techniques to identify root causes, trends, and cross-domain performance drivers.
• Use advanced methods such as clustering, time-series decomposition, and seasonality analysis to detect irregular or emerging behavior.

• Develop and maintain cloud-native analytics solutions using Azure Databricks, Power BI, Splunk, Snowflake, and Spark clusters.
• Implement CI/CD practices for analytics and ML pipelines, including version control, automated testing, orchestration, and deployment controls.
• Build reusable data components and frameworks that promote consistency, accessibility, and shared ownership.

• Collaborate with data engineers on schema design, performance tuning, and scalable architecture.
• Partner with product owners and network teams to translate business and operational needs into clear, inclusive technical requirements.
• Contribute to Agile ceremonies and planning discussions, helping shape solutions from idea to production.

• A Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field-or equivalent practical experience.
• 2+ years of experience in data analytics, data engineering, or building cloud-native data products.
• Proficiency in:

• SQL (CTEs, window functions, performance tuning, distributed queries)
• Python (pandas, PySpark, automation, ML libraries)
• Spark / PySpark (distributed processing, UDFs, Delta Lake)
• Data modeling (star/snowflake schemas, time-series, wide and normalized designs)
• Experience working with structured, semi-structured (JSON), and unstructured data at scale.
• Familiarity with cloud and analytics platforms such as Azure Databricks, Synapse, Snowflake, Power BI, Splunk.
• Knowledge of networking and telemetry concepts, including:

• Access technologies (Wi-Fi, DOCSIS, PON, LTE, Zigbee)
• Device telemetry protocols (TR-069, TR-181, TR-104)
• Performance metrics (latency, throughput, signal quality, error rates)
• Comfort with Git-based workflows, CI/CD pipelines, automated validations, and job orchestration tools.
• Strong analytical, problem-solving, and debugging skills-especially when working with noisy or complex datasets.

• Experience with machine-learning frameworks (scikit-learn, TensorFlow, XGBoost).
• Exposure to streaming platforms (Kafka, Event Hubs, IoT Hub - streaming focus only).
• Understanding of broadband access networks, RF behavior, Customer Premise Equipment performance, or service-provisioning systems.
• Familiarity with observability and monitoring tools (Prometheus, Grafana, ELK).

Rogers Communications