Data Engineer | AWS | Databricks | Python | SQL
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As a Technical Lead at Capgemini Engineering, I leverage my AWS certifications and my skills in AWS Cloud, Snowflake, DataBricks, Python and Airflow to design, build, and maintain scalable and reliable data pipelines and solutions for various clients. I have over 17+ years of experiences overall and 10+ years of experiences in data engineering , and I have worked on projects involving data ingestion, processing, transformation, analysis, and visualization using various tools and frameworks. I am passionate about delivering high-quality data products that enable data-driven decision making and insights.
I enjoy collaborating with cross-functional teams, learning new technologies, and solving complex data problems. I am always looking for new opportunities to grow my skills, contribute to the data community.
With a mission to deliver impactful data-driven solutions, I lead a team dedicated to enabling seamless batch data processing for non-streaming business areas. By leveraging cloud technologies and metadata-driven custom ETL engines, we transform and integrate large datasets into actionable insights. Holding multiple AWS certifications, I am committed to driving innovation and operational excellence in data engineering.
The Non-Streaming Data Processing ingests and processes data from various non-streaming business areas, including Theatrical, Content Production, Franchise, Tours, Retail, Sports Analytics, and CNNi. It supports key data use cases like studio data science, marketing campaign performance, audience insights, and ticketing. The platform also integrates data from sources such as Gracenote, Salesforce, Rotten Tomatoes, and Adobe Analytics, ensuring comprehensive data availability for business intelligence and analytics.
Batch processing occurs in Databricks and Snowflake, orchestrated by Airflow, providing insights for stakeholders across multiple domains.
Technology: Python, ETL, AWS, Airflow, Databricks, Snowflake, SQL, Git, Shell Script, Linux, PyCharm
