Lead DevOps Engineer
Great Eastern · Cuenca, Azuay, Ecuador
Insurance · 1,001-5,000 employees
About the role
This role manages data ingestion pipelines from various sources to the data lake while automating validation and reporting processes. The lead engineer also performs production support, conducts code reviews, and optimizes system performance to meet established SLAs.
What they look for
Requirements
Candidates must possess a Bachelor or Master degree in Computer Science or Engineering and at least 8 years of experience in data engineering with the Hadoop stack. Proficiency in Scala, PySpark, ETL tools, and RDBMS is required, along with strong scripting skills in Linux and SQL.
Full description
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.
