Data Engineer Real-Time

Praveen Aluru

Data Engineer

KANSAS *******.*****@*******.*** +1-913-***-**** LINKEDIN

SUMMARY

Data Engineer with 4+ years of experience designing and optimizing large-scale data pipelines, data lakes, and cloud data platforms to power real-time analytics and advanced insights. Skilled in ETL/ELT design, streaming architectures, and data modeling using Spark, Airflow, Kafka, Snowflake, and cloud-native services across AWS, Azure, and GCP. Proven success in reducing data latency by up to 50%, cutting cloud storage costs by 30%, and improving BI performance by 40%. Experienced in DevOps automation, big data ecosystems, and ML-ready infrastructure, supporting organizations to make faster, data-driven decisions. CORE COMPETENCIES

Data Engineering & Big Data: ETL/ELT Pipelines, Apache Spark, Databricks, Airflow, Hadoop, Hive, dbt Cloud Platforms: AWS (Glue, Redshift, Lambda, S3), Azure (Data Factory, Synapse Analytics), GCP

(BigQuery, Dataflow)

Streaming & Real-Time Analytics: Apache Kafka, Event-Driven Architectures Programming & Databases: Python, SQL (T-SQL, PL/SQL, PostgreSQL), Scala, NoSQL (MongoDB, DynamoDB)

Data Warehousing & Modeling: Snowflake, Azure Synapse, Redshift, Data Lakes DevOps & Automation: Docker, Kubernetes, Terraform, CI/CD Pipelines Visualization & Monitoring: Power BI, Tableau, AWS CloudWatch, Prometheus PROFESSIONAL EXPERIENCE

Data Engineer, Clairvoyant Jan 2025 – Present

• Accelerated analytics by 45% by building and automating ETL/ELT pipelines using Spark, Airflow, and cloud-native tools (AWS Glue, Azure Synapse, BigQuery) for 10+ TB of data.

• Reduced cloud storage costs by 30% through Snowflake optimization and data partitioning strategies, improving query speed for BI dashboards used by 500+ stakeholders.

• Enabled real-time decision-making by designing Kafka-based streaming frameworks, processing millions of daily events, and integrating with ML workflows for fraud detection.

• Developed Python-based data ingestion scripts for PDF/Excel extraction, deployed on serverless platforms (Lambda/Azure Functions), ensuring 99.9% pipeline uptime.

• Migrated on-prem data warehouses to cloud-native platforms (Snowflake, BigQuery), eliminating legacy dependencies and enabling AI-driven data workflows.

• Implemented monitoring and alerting using CloudWatch and Prometheus, cutting incident resolution time by 40%.

Data Engineer, Avenir Technologies Jan 2020 – Jul 2023

• Reduced batch processing time by 40% by building event-driven data pipelines using Airflow, dbt, and Spark, supporting real-time analytics for business-critical dashboards.

• Designed and implemented data models and warehouses in Snowflake and Databricks, improving query performance by 35% for analytical workloads.

• Optimized SQL (T-SQL, PL/SQL, PostgreSQL) queries and schema design, reducing report generation time by 25% for finance and operations teams.

• Built self-healing ingestion frameworks with data validation and alerting, improving data quality by 98% and reducing downtime incidents to near zero.

• Containerized data workloads with Docker and Kubernetes, and deployed infrastructure using Terraform and CI/CD pipelines, reducing deployment time by 70%.

• Delivered interactive dashboards using Power BI and Tableau, accelerating reporting cycles and enabling data-driven decision-making across departments. EDUCATION

Masters in Computer Science, University of Central Missouri

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