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!
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.