Ijaspid Kenna
@ijaspidkenna
Senior Data Engineer specializing in cloud infrastructure and automation.
What I'm looking for
Senior Data Engineer with 11 years of experience designing and implementing scalable, cloud-native data solutions across AWS, Azure, and Google Cloud Platform (GCP). Proven expertise in data modeling, advanced SQL, and Python, with deep proficiency in ETL and orchestration tools such as Apache Airflow,
Azure Data Factory, and dbt. Skilled in building automated, high-throughput data pipelines and leading large-scale data migrations to modern cloud data warehouses like Snowflake and BigQuery. Adept at optimizing data processing workflows, enhancing data quality and accessibility, and delivering actionable
insights to drive business intelligence and analytics across cross-functional teams.
Experience
Work history, roles, and key accomplishments
Senior Data Engineer
Shiro Technologies LLC
Jun 2018 - Present (7 years)
Engineered real-time data ingestion frameworks using Python, AWS Kinesis, and Apache Kafka, enabling low-latency analytics for financial transactions, fraud detection, and risk assessment
workflows. Integrated Databricks with AWS Redshift and RDS, implementing federated queries and query tuning strategies that boosted reporting speed by 45% for BI and finance teams.
Data Engineer
CDI Technology Solutions
Sep 2014 - May 2018 (3 years 8 months)
Engineered and maintained high-throughput data ingestion pipelines in Snowflake, using Snowpipe and auto-scaling warehouses to support real-time analytics on streaming
data with minimal latency. Improved SQL query performance for AWS Redshift and PostgreSQL by applying advanced indexing, partitioning, and tuning techniques, reducing average query execution time by 50%.
Education
Degrees, certifications, and relevant coursework
Temple University
Bachelor of Business Administration, Management Information Systems
2014 - 2018
Studied Management Information Systems, focusing on the integration of technology and business processes to improve organizational efficiency and effectiveness.
Tech stack
Software and tools used professionally
Splunk
AWS Glue
Amazon EC2
Amazon CloudWatch
GitHub
GitLab
Kubernetes
Amazon EKS
AWS CodePipeline
Jenkins
CircleCI
GitHub Actions
GitLab CI
Hadoop
AWS CodeDeploy
Gmail
Node.js
Next.js
.NET
Terraform
AWS CloudFormation
CDI
jQuery
JavaScript
Java
Logstash
Kafka
PagerDuty
VictorOps
Grafana
Kibana
Prometheus
Linux
Windows
New Relic
Datadog
GraphQL
Elasticsearch
Solr
Ansible
AWS Lambda
Airflow
SQL
TeamCity
ServiceNow
Availability
Location
Authorized to work in
Skills
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