Big Data Engineer

Role & responsibilities

Integral team member of our Data Engineering team responsible for design and development of Big data solutions Partner with domain experts, product managers, analyst, and data scientists to develop Big Data pipelines in Hadoop or Snowflake Responsible for delivering data as a service framework

• Responsible for moving all legacy workloads to cloud platform
• Work with data scientist to build Client pipelines using heterogeneous sources and provide engineering services for data science applications
• Ensure automation through CI/CD across platforms both in cloud and on-premises
• Ability to research and assess open source technologies and components to recommend and integrate into the design and implementation
• Be the technical expert and mentor other team members on Big Data and Cloud Tech stacks
• Define needs around maintainability, testability, performance, security, quality and usability for data platform
• Drive implementation, consistent patterns, reusable components, and coding standards for data engineering processes
• Worked on languages like Python, PySpark, Scala to execute on Hadoop and non-Hadoop ecosystems
• Tune Big data applications on Hadoop and non-Hadoop platforms for optimal performance
• Evaluate new IT developments and evolving business requirements and recommend appropriate systems alternatives and/or enhancements to current systems by analyzing business processes, systems and industry standards.
• Supervise day-to-day staff management issues, including resource management, work allocation, mentoring/coaching and other duties and functions as assigned

Skills:

5-7 years of experience:

Experience in hadoop/big data technologies.

Experience in spark.

2+ year of experience working on Google or AWS cloud developing data solutions. Certifications preferred.

Hands-on experience with Python/Pyspark/Scala and basic libraries for machine learning is required;

Experience with containerization and related technologies (e.g. Docker, Kubernetes)

Experience with all aspects of DevOps (source control, continuous integration, deployments, etc.)

Advanced knowledge of the Hadoop ecosystem and Big Data technologies Hands-on experience with the Hadoop eco-system (HDFS, MapReduce, Hive, Pig, Impala, Spark, Kafka, Kudu, Solr)