Data Engineer

Talent Insider is an upcoming HR Consultancy Service, founded in 2021. Our clients have been some of the leading brands in Indonesia, and this service continues to expand.

Registered in Singapore & Indonesia, we can assist with your growth plans and strategies, and continue to expand our regional presence with strong regional partners to assist our client in recruitment and branding strategy.

•  Create And Maintain Optimal Data Pipeline Architecture
• Assemble Large, Complex Data Sets That Meet Functional / Non-functional Business Requirements.
• Identify, Design, And Implement Internal Process Improvements: Automating Manual Processes, Optimizing Data Delivery, Re-designing Infrastructure For Greater Scalability, Etc.
• Build The Infrastructure Required For Optimal Extraction, Transformation, And Loading Of Data From A Wide Variety Of Data Sources Using SQL And AWS 'big Data' Technologies.
• Build Analytics Tools That Utilize The Data Pipeline To Provide Actionable Insights Into Customer Acquisition, Operational Efficiency And Other Key Business Performance Metrics.
• Work With Stakeholders Including The Executive, Product, Data And Design Teams To Assist With Data-related Technical Issues And Support Their Data Infrastructure Needs.
• Create Data Tools For Analytics And Data Analyst That Assist Them In Building And Optimizing Our Product Into An Innovative Industry Leader.
• Work With Data And Analyst To Strive For Greater Functionality In Our Data Systems.
• Build Reports And Dashboards To Support The Data Needs Of Business Teams.

• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
• Experience building and optimizing 'big data' data pipelines, architectures and data sets.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
• Strong analytic skills related to working with unstructured datasets.
• Build processes supporting data transformation, data structures, metadata, dependency and workload management.
• A successful history of manipulating, processing and extracting value from large disconnected datasets.
• Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores.