Venkat Praneeth
@venkatpraneeth
Senior Data Engineer specializing in scalable cloud-native data platforms and real-time analytics.
What I'm looking for
I am a Senior Data Engineer with 6+ years building large-scale, cloud-native data platforms and real-time analytics solutions across healthcare, finance, e‑commerce, and M&A domains.
I design and implement scalable ELT/ETL pipelines using Snowflake, Redshift, Databricks, AWS Glue, and Airflow, and I modernize legacy systems by migrating Teradata, Oracle, and Hadoop workloads to cloud data platforms.
I build low-latency streaming solutions with Kafka, Kinesis, Flink, and Spark Streaming, and I enable ML workflows using MLflow, SageMaker, feature stores, and curated feature datasets for fraud scoring, clinical risk, and document intelligence.
I focus on governance, data quality, and cost optimization—implementing Great Expectations, dbt tests, lineage, RBAC, and infrastructure-as-code (Terraform/CloudFormation/GitHub Actions) to deliver reliable, compliant, high-performing analytics for stakeholders.
Experience
Work history, roles, and key accomplishments
Senior Data Engineer
IMCO
Aug 2023 - Present (2 years 7 months)
Developed real-time Kafka+Spark pipelines and Snowflake-based analytics to power VDR document intelligence, reducing dashboard latency and improving dataset reliability for compliance and product teams.
Data Engineer
Toyota Motors
Feb 2021 - Aug 2023 (2 years 6 months)
Designed secure AWS-based lakehouse and streaming pipelines for healthcare data, improving governance and reducing dashboard refresh times by 35% through Redshift/Snowflake tuning.
Built and maintained Azure Synapse data warehouse and ETL workflows, migrating on-prem Oracle data to cloud and implementing DBT-based logical transformations to support Tableau reporting.
Education
Degrees, certifications, and relevant coursework
Jawaharlal Nehru Technological University, Kakinada
Bachelor in Computer Science, Computer Science
Completed a Bachelor in Computer Science from JNTU Kakinada, graduating in March 2019.
Tech stack
Software and tools used professionally
Amazon Redshift
Azure Synapse
Apache Spark
AWS Glue
Talend
Amazon Redshift Spectrum
SAS
Amazon Quicksight
AWS IAM
Microsoft Azure
Google Cloud Platform
Amazon S3
AWS Step Functions
GitHub
Kubernetes
AWS CodePipeline
Jenkins
GitHub Actions
Amazon SageMaker Pipelines
Jupyter
NumPy
Pandas
PySpark
AWS Glue DataBrew
dbt
DB
Sqoop
PostgreSQL
MongoDB
Cassandra
Hadoop
HBase
Gmail
Rollout
Django
Spring Boot
Yarn
Databricks
Terraform
PyCharm
Jira
JavaScript
Java
JSON
Perl
PowerShell
TensorFlow
MLflow
scikit-learn
Kafka
Apache NiFi
Ambari
Amazon DynamoDB
Zookeeper
Amazon Kinesis
OpenSearch
AWS Lambda
Amazon RDS
Amazon Aurora
Azure SQL Database
Notepad++
Airflow
Time Analytics
erwin Data Modeler
Amazon Web Services (AWS)
Amazon Athena
SQL
Core Data
Amazon SageMaker
Azure Blob Storage
SciPy
AWS KMS
Amazon EventBridge
Delta Lake
Great Expectations
Collibra
Cosmos
Enhance
Polybase
Dynamic
Task
Factory
Safe
Movement
Availability
Location
Authorized to work in
Job categories
Skills
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