A solid Apache Spark sample resume will help you establish a foothold in the competitive world of big data, and analytics. Employers want candidates who can design, develop and optimize data processing pipelines, using Apache Spark. If you are a data engineer, or developer transitioning to Spark, a great Spark developer resume should showcase your technical skills, project experience and, capabilities with large data. In this article, we offer Apache Spark resumes and tips to write an ATS-friendly document, to attract attention.
TAYLOR SMITH
Chennai, India | adking@gmail.com | 555-5555-55
CAREER OBJECTIVE
Focused and detail-oriented Software Engineer having around 13 years of experience in the IT industry with a passion for sharing knowledge and enthusiastic learner of new technologies in the domain of Big Data and Data Science.
EDUCATION
Course (Stream)/ Examination | Institution/University/School | Year of Passing | Performance |
| MCA in Computer Science | College Name-Location | 2005 | 85% |
| HSC | School Name-Location | 2000 | 77% |
| SSLC | School Name-Location | 1998 | 80% |
SKILLS
- Apache Spark (4 years)
- Scala (3 years), Python (1 year)
- Core Java (5 years), C++ (6 years)
- Hive (3 years)
- Apache Kafka (3 years)
- Cassandra (3 years), Oozie (3 years)
- Spark SQL (3 years)
- Spark Streaming (2 years)
- Apache Zeppelin (4 years)
PROFESSIONAL EXPERIENCE
Apache Spark developer
Company Name-Location – July 2012 to May 2017
Roles & Responsibility:
- Built Spark Scripts by utilizing scala shell commands depending on the requirement.
- Responsible for developing scalable distributed data solutions using Hadoop.
- Involved in performance tuning of spark applications for fixing right batch interval time and memory tuning.
- Using the memory computing capabilities of spark using scala, performed advanced procedures like text analytics and processing.
- Involved in development of automated scripts to install Hadoop clusters.
- Responsible for troubleshooting and resolving the issues related to performance of Hadoop cluster.
Software Engineering
Company Name-Location – May 2008 to July 2012
Roles & Responsibility:
- Implemented Survival regression for a healthcare customer.
- Performed optimization of multiple Spark jobs.
- Implemented a data pipeline for ingesting Workforce data into cluster.
- Participated in a multiple big data POC to evaluate root cause of Overtime for healthcare domain.
- Implemented a pipeline for calculation of important index for retail customer data.
- Performed visualization of analyzed data using Bokeh library in Zeppelin notebook.
Sr. Software Engineer
Company Name-Location – June 2005 to May 2008
Roles & Responsibility:
- Design and implemented Multi-thread XML library using Expat XML parser for parsing Service data, received from HSS.
- Implementation of service provisioning framework in Application server.
- Developed SIM module for interacting with HSS.
- Carried out debugging and profiling activity for improving performance of the product.
ADDITIONAL INFORMATION
Technical Strengths:-
- Programming Languages : C, Java, Python.
- Application Servers : Tomcat, Web sphere.
- Data bases : Microsoft SQL server, DB2.
- Operating systems : UNIX, LINUX.
- Hadoop Technologies : Spark, Hadoop, Flume.
Accomplishments:-
- Established a team to support regional operations.
- Troubleshooting the issues and finding the defects.
Download our Apache Spark sample resume in Word format and edit it for your next job application. This clean ATS-friendly outline allows you to highlight your Spark skills, programming ability, and data processing accomplishments when applying for jobs, and will help you impress Recruiters!
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The best Apache Spark example resume lists your technical skills, but also illustrates your effectiveness with data, scalability and performance. A targeted Spark developer resume must display your accomplishments, measurable results and applicable certifications. You can refer to tried and true Apache Spark resumes and use a professional Word template, to show employers your aptitude, and, in a short amount of time, get more interviews in the fast-growing big data industry.
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