03 / TELECOMMUNICATIONS
Enterprise Telecom Data Processing & Analytics
Developed and maintained enterprise data pipelines for telecommunications network, subscriber, billing, and operational datasets. PySpark transformations and quality controls prepared curated data for reporting and analytics.
Sasken Technologies · Junior Data Engineer · June 2018 – July 2021THE CONTEXT
A clear starting point.
As Junior Data Engineer at Sasken Technologies from June 2018 to July 2021, I developed ETL/ELT workflows and worked with analysts, reporting teams, and business stakeholders on mappings, processing rules, and reporting datasets.
THE CHALLENGE
What needs to work.
API, FTP, and enterprise sources arrived as CSV, JSON, and Parquet. Nested operational logs and timestamps across regions required standardization. Malformed records, missing values, and billing inconsistencies needed to be detected before affecting downstream reporting.
THE APPROACH
Inside the decisions.
Ingest diverse source formats
Built Azure Data Factory ingestion workflows for APIs, FTP sources, and enterprise datasets into Azure Data Lake Storage and big data environments. Used source-to-target mappings and profiling to define consistent data flows.
Transform operational data
Developed PySpark and Spark SQL processing to parse and flatten nested JSON, normalize timestamps, and perform joins, aggregations, and filtering across subscriber usage, billing, and operational data.
Validate and reconcile
Applied schema enforcement, datatype checks, null validation, duplicate detection, and record-level filtering. Quality-control and reconciliation frameworks identified usage anomalies, billing inconsistencies, and invalid records.
Prepare reporting datasets
Supported curated data for network usage, billing behavior, fraud indicators, churn-related metrics, and executive KPI reporting. Worked with analysts to align transformations with reporting requirements.
Optimize and operate
Used broadcast joins, caching, and partition pruning to tune Spark workloads. Supported orchestration, retries, dependencies, monitoring, logging, alerting, production troubleshooting, and controlled data access.
THE TOOLKIT
OUTCOMES & VALIDATION
Evidence over assumptions.
Delivered standardized, validated datasets for telecommunications analytics and reporting. Reconciliation controls supported data trust, while workload tuning, orchestration, and monitoring improved maintainability and operational reliability.
Operational reporting starts with consistent source data. Validation and traceable transformations make curated datasets easier to trust.