Sivakumar Dayalan

I’m a data engineer who enjoys building systems that people can trust — systems that run quietly in the background, handle scale without drama, and make data genuinely useful. Over the last several years, I’ve worked on designing and optimizing big data pipelines, mainly using Spark, PySpark, and cloud-based platforms, solving problems that sit at the intersection of data, performance, and reliability.

What I care most about is how things are built. I like simplifying complex workflows, improving efficiency, and making pipelines easier to operate and maintain. Whether it’s tuning a slow Spark job, automating a workflow, or helping a team think through a better data model, I enjoy getting into the details and making lasting improvements.

I believe good data engineering is not just about tools or scale — it’s about responsibility, clarity, and impact. I’m continuously learning, exploring cloud-first architectures, and looking for opportunities where thoughtful data engineering can make a real difference.

• Data Engineering & Big Data: Apache Spark (Scala, PySpark), Hadoop, Hive, Kafka, Druid, Solr, Sqoop
• Cloud: AWS (S3, EMR, RDS, EC2, MSK, Glue), GCP (BigQuery, Cloud Storage, Dataproc, Cloud Composer), Azure (ADLS, ADF, EH and Databricks)
• Programming: Python, Scala, SQL, Shell Scripting
• ETL & Orchestration: Airflow, Control-M, NiFi
• Databases: DB2, PostgreSQL, MySQL, Oracle, Teradata
• Tools: Git, Jenkins, Docker, Kubernetes
• AI/ML Integration: Data preparation, batch inference support, model pipeline integration

• Bachelor of Engineering (Computer Science) | Anna University | 2012