Sai Praneeth K
Sr Data Engineer @Continental Resources
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WORK HISTORY
Sr Data Engineer @Continental Resources
OK, US
Designed and developed high-performance Spark applications (Scala, Spark SQL, Spark MLlib, RDDs, DataFrames) to process large volumes of structured, semi-structured, and unstructured data, improving job execution efficiency by 30%.Built real-time streaming data pipelines using Spark Streaming and Kafka to ingest and process XML, Avro, and JSON data in near real time, persisting results into Cassandra for low-latency analytics.Implemented end-to-end Azure Databricks pipelines, provisioning clusters, developing notebooks, and integrating with Azure Data Lake, SQL Database, and Azure ML, enabling predictive analytics and seamless collaboration with data science teams.Migrated complex on-premise Hadoop workloads to AWS (EC2, S3, EMR), including data ingestion from RDBMS, transformations in Spark, and storage in S3/Hive external tables for downstream BI and reporting.Created and optimized ETL workflows using Python, Ab Initio, Informatica, and Sqoop to load data from RDBMS into Hadoop/Hive; developed Hive UDFs and Impala tables to improve query performance and support Tableau dashboards.Automated data ingestion and orchestration by building Airflow DAGs to schedule and monitor Hive, Pig, and Spark jobs; implemented custom Pig loaders to handle complex JSON and compressed CSV file formats.Partnered with business stakeholders to analyze requirements, design data pipelines, and deliver reporting solutions, ensuring alignment with business needs and data governance standards.Environment: Agile Scrum, Hadoop, Spark, Scala, Spark SQL, Spark Streaming, Kafka, Hive, Pig, Sqoop, Airflow, AWS (S3, EMR, EC2), Azure Databricks, Snowflake, Python, Ab Initio, Informatica, Tableau.
EDUCATION
BV Raju Institute of Technology (BVRIT)
Bachelors, Computer Science
University of North Texas
Masters, Data Engineering
ABOUT SAI PRANEETH K
I am a Senior Data Engineer with 11+ years of experience building and optimizing large-scale, cloud-native data ecosystems that power analytics, AI, and business transformation. Over the course of my career, I’ve designed and delivered end-to-end data pipelines, data lakes, and warehouses leveraging both AWS (Redshift, EMR, Glue, S3, Lambda) and Azure (Data Factory, Databricks, Synapse, Data Lake, Event Hubs).My expertise spans big data frameworks (Spark, Hadoop, Hive, Kafka, Flume, Storm), programming (Python, Scala, SQL), and databases (Snowflake, Teradata, Oracle, NoSQL). I specialize in building real-time streaming solutions, high-performance ETL/ELT workflows, and scalable architectures that handle structured, semi-structured, and unstructured data.Throughout my journey, I’ve:Built streaming data pipelines using Spark Streaming, Kafka, and Flume for real-time analytics.Migrated complex on-prem applications to cloud environments (AWS & Azure), improving scalability and reducing costs.Designed data ingestion frameworks and orchestration pipelines with Airflow, Oozie, and Ab Initio.Developed automation frameworks and reusable data solutions with Python and Shell scripting to streamline production support.Collaborated with cross-functional teams in Agile environments, delivering zero-defect code and improving business decision-making.Beyond technical skills, I am passionate about data-driven innovation—transforming raw data into trusted, actionable insights. I thrive at the intersection of engineering, cloud architecture, and analytics, enabling businesses to modernize their data strategies and unlock measurable value.I’m always open to connecting with data professionals, technology leaders, and innovators to share knowledge and explore opportunities in the evolving world of cloud data engineering and AI-driven analytics.
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