Teja K

About Teja K

Data specialist with 5+ years of experience designing and implementing scalable data pipelines and analytics solutions across cloud platforms including GCP and AWS, Guidewire. Skilled in BigQuery, Airflow, Apache Kafka, and Python with a strong background in real-time and batch ETL processing.Led multiple projects leveraging Google Cloud services like Cloud Storage, Dataflow, Pub/Sub, and BigQuery for building high-throughput data platforms. Passionate about optimizing data architecture for analytics and business insights.Currently focused on cloud-native data engineering, data warehousing, and building resilient distributed data systems. Open to opportunities in cloud data engineering and platform modernization.

Experience

1. Data Engineer

   Definity

   Mar 2023 — Present · Waterloo, ON, CA

   Responsibilities:Contributed to the successful implementation of Canada’s first P&C insurance claims management system by integrating Guidewire with enterprise-scale GCP data pipelines.Collaborated with Integration Architects to design and optimize end-to-end data flows aligned with business use cases and cloud-native architectural principles.Built and deployed GCP-native ETL/ELT pipelines using Cloud Composer (Airflow), Cloud Functions, and Dataflow for orchestrating data ingestion, transformation, and delivery into BigQuery.Developed CDC (Change Data Capture) workflows using Databricks to ingest log data from AWS into Google Cloud Storage (GCS), enabling real-time analytics and compliance reporting.Integrated data from diverse internal and third-party systems including Guidewire, Salesforce, Thomson Reuters Legal Tracker API, and Verisk.Managed and transformed various data formats such as CSV, JSON, XML, XLSX, flow data, and vehicle telematics.Designed data pipelines to support regulatory reporting (e.g, Risk Sharing Pool) and business units including General Ledger, Broker Operations, Corporate Actuarial, and Incentive Systems.Automated schema validation, schema evolution handling, and ingestion workflows using Python-based Airflow DAGs.Utilized Pub/Sub for real-time event ingestion and optimized BigQuery datasets for cost-efficient analytics using partitioning and clustering strategies.Developed SQL routines to validate data, derive business metrics, and integrate with Looker Studio for visualization.Worked closely with DevOps and Security teams to provision lower environments and ensure compliance with IAM and cloud security best practices.Performed data profiling, anomaly detection, and quality validation using PySpark on Dataproc, enhancing pipeline reliability and reducing operational incidents.Supported hybrid cloud integration by migrating source data from AWS to GCP BigQuery.

Education

• Conestoga College

  Big data solutions Architecture

  2021 — 2022

• JB Institute Of Engineering and Technology (JBIET)

  Bachelor of Technology - BTech

  2013 — 2017