Lead DevOps Engineer

This role oversees and manages the ingestion framework to ingest data from various data sources for data analytics and AI purposes. Detailed activities include:

• Analyzing the data requirements across various entities, develop, implement and maintain data ingestion pipelines from source to data lake pipelines.
• Automating data validation steps and report generation.
• Automating codes/ scripts that can be repeatedly used across similar analytics and reporting requirements.
• Managing and ensuring accuracy and timeliness and automation solution in end to end data extraction and integration with analytics and management reporting system.
• Designing the jobs schedule in scheduling tools and developing the configuration files for job schedulers.
• Performing production L2 batch support after production deployment.
• Perform code review functions for applications / programs developed by team members.
• To be part of initiatives that brings data into the data lake and delivers insights.
• Monitor and measure performance to assure ongoing data ingestion is meeting the SLA and optimization of the ingestion process to manage the performance and the SLAs.
• Work effectively with other stakeholders such as data engineering team, IT team, etc.
• Troubleshoot MapReduce/Spark Jobs and do performance tuning in production environments.
• Independently develop and sustain technical knowledge, certifications, and skills.
• Effectively handling day-to-day assignments given moderate directions and supervision.

• Bachelor or Master Degree in Computer Science, Engineering, or similar relevant field.
• Working experience in data ingestion or data engineering with Hadoop tech stack for 8+ years.
• Proficient with Scala and PySpark.
• Hands-on experience on Spark framework and other distributed data processing frameworks like Hadoop Map-Reduce, Hive etc. Proficient in ETL tools like Talend.
• Proficient in RDBMS databases such as Oracle, MySQL, MSSqlServer.
• Strong scripting skills in Linux environment and SQL.
• Expertise in Hadoop ecosystems.
• Hands-on Experience in Sqoop, Hive, Spark, Python, Scala is a must.
• Hands-on Experience in Job orchestration / Job schedulers like Autosys, Control-M
• Good to have working experience with one of the cloud platforms like AWS (Amazon Web Services), Microsoft Azure or Google Cloud Platform.
• Ability to plan and organize technical work and deliverables.
• Ability to follow guidelines and adhere to the established software development standards and conventions.

• S

elf-motivated and independent.

• Able to work with minimum supervision and to work well with stakeholders and project staff.
• Ability to prioritize and multi-task across numerous work streams.

• S

trong interpersonal skills; ability to work on cross-functional teams.

• Strong verbal and written communication skills.
• Deep knowledge of best practices through relevant experience across data-related disciplines and technologies particularly for enterprise wide data architectures and data warehousing/BI.
• Demonstrated problem-solving skills. Ability to learn effectively and meet deadline.