Headline

Data Engineer at Infolob | Google Cloud Platform (GCP) | Azure DataBricks | Python | SQL | Pyspark | Big Data | Big Query | Airflow composer | Data Warehouses | ADF | Delivered 20–40% Performance Optimization

About

I'm a results-driven Data Engineer with ~2.5 years of experience in turning raw, scattered data into high-performing, insight-ready systems. My expertise lies in building scalable ETL pipelines, designing cloud-native data architectures, and modernizing data platforms to deliver faster analytics and better decisions. At Infolob, I’ve contributed to large-scale projects across GCP, Azure, and AWS, helping global clients streamline their data ecosystems. My work includes building robust pipelines using Python, PySpark, and SQL, and integrating tools like Databricks, Glue, Snowflake, BigQuery, and Cloud Storage for seamless data processing and transformation. I have hands-on experience with data orchestration tools such as Cloud Composer (Airflow) and Control-M, and I automate workflows using CI/CD pipelines via GitHub. From handling migrations (like MySQL to PostgreSQL), optimizing costs with partitioning and clustering, to enabling business-ready dashboards in Power BI, I make data usable, fast, and efficient. I also specialize in data quality checks, stored procedures, materialized views, and pandas-based validations before data ingestion. Whether it’s Oracle, MySQL, or PostgreSQL, I ensure performance, accuracy, and clarity in every layer of the data stack. What sets me apart? I don’t just move data—I build intelligent, reliable systems that power real-time decisions across operations, marketing, and finance. And behind the scenes—I provide freelance support and backend debugging help for early-career professionals and data teams, helping them optimize pipelines and ship production-ready solutions with confidence.

Location

Hyderabad

Work experience

Data Engineer at INFOLOB, Aug 2025 - Dec 2025 · 5 mos, Hyderabad, Telangana, India, At Infolob, I designed and implemented scalable ETL pipelines across GCP and Azure ecosystems using BigQuery, Cloud Functions, GCS, Azure Data Factory, and Apache Spark. I improved data integration efficiency by 20% and enabled near real-time data processing through optimized architecture design. By applying Spark performance tuning techniques such as broadcast joins, caching, salting, and distributed workload optimization, I reduced job execution time from 4 hours to 2.4 hours and improved data retrieval speed by 30%. I also implemented a metadata-driven ingestion framework and Delta Lake Bronze/Silver/Gold architecture, strengthening data reliability and enhancing deployment efficiency through CI/CD automation with GitLab.

Data Engineer at Heeddata, Inc., Jul 2023 - Sep 2025 · 2 yrs 3 mos, Hyderabad, Telangana, India, At Heeddata, I supported and optimized production data workflows by resolving job and DAG failures across Airflow, Control-M, Ab Initio, and BigQuery environments, ensuring SLA compliance and minimizing business disruptions. I enhanced CI/CD validation processes by integrating automated testing and root cause analysis practices, improving deployment stability and early defect detection. Additionally, I implemented proactive monitoring and alerting mechanisms that reduced manual intervention time by 40% and accelerated incident resolution across critical data pipelines.

Application Development Analyst at Heeddata India private limited, Jul 2023 - Sep 2025 · 2 yrs 3 mos, Handled failure tickets related to job failures, DAG failures, and application failures within defined SLA timelines. Enhanced continuous data validation practices by embedding TOSCA into CI/CD systems. Performed root cause analysis (RCA) by backtracking the data flow and identifying sources of failures to implement permanent fixes. Investigated issues using Control-M, Ab Initio, and Airflow, analyzing logs and verifying job/DAG status. Ran Airflow DAGs manually in case of failures or missing/misprocessed data, ensuring timely job completion. Used BigQuery to analyze, validate, and correct data issues, including removing duplicate records and fixing inconsistent data. Troubleshot and resolved errors in the Git CI/CD pipeline, applied code fixes, and ensured smooth deployments. Collaborated with cross-functional teams to investigate, troubleshoot, and resolve complex production issues efficiently. Proactively conducted root cause analysis, implemented solutions, and ensured SLA commitments were met, minimizing business impact. Collaborated with development and QA teams to define automated data quality checks and integrate them within TOSCA-driven pipelines, improving defect detection rates early in the deployment cycle. Streamlined incident response by designing monitoring scripts and alerts for Airflow and Control-M jobs, reducing manual intervention time by 40%. Documented recurring job failures and their resolutions in a centralized knowledge base, accelerating onboarding and improving support team efficiency.

Education

Bachelor's degree, Mathematics and Computer Science, Sri Ramakrishna Degree College (Autonomous)

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