Job Summary:
A Data Engineer is responsible for designing, building, and maintaining large-scale data systems that enable data-driven decision-making.
They work with various stakeholders to understand data requirements, data architectures, and implement data pipelines to support business intelligence, analytics, and data science initiatives.
Required Experience:
* Bachelor’s Degree in MIS/Engineering/Computer Science
* Data Warehousing / BI Certification a plus
* Advanced in SQL
* 5+ Experience working with at least two of the top ETL tools: DBT, PWC, ODI, Datastage, DBT (mandatory)
* 3+ years working with cloud environments AWS, Azure
* 3+ years working with Apache Airflow
* 5+ years working in an IT function
* 5+ years of BI development, analyst, data modelling, and support experience
* 5+ years of Relational Database Oracle, SQL Server,
or
* 5+ years of Columnar Database Redshift
* 3+ years of Spark, Glue, EMR
* 3+ Experience in scalable python development, (PySpark, Spark SQL)
* 2+ Experience working with at least two of the top BI tools: Tableau, Qlik, OBIEE
* Flexible to adapt and quickly (willing to) learning different technologies.
Other Requirements
* English C1
* Ability to cooperate and work in multicultural environment
* Communication and teaching oriented, knowledge transfer ability.
* Multi-tasking ability – handling multiple activities in parallel
* Organized and structured
* Be updated on Scrum methodology
* Proactive, flexible, result-driven, with a “can do” attitude, attention to detail, problem-solving
Scope of Services:
Data Pipeline Development
* Build and maintain data pipelines to extract, transform, and load (ETL) data from various sources.
Data Quality & Reliability
* Implement data quality checks and validation processes.
* Ensure data accuracy, consistency, and reliability across systems.
System Optimization & Performance
* Optimize data systems for performance, scalability, and reliability.
* Troubleshoot and resolve data-related issues and system problems.
Collaboration & Requirements Gathering
* Work closely with data scientists, analysts, architects, and other stakeholders to understand data requirements and deliver appropriate solutions.
Continuous Learning & Innovation
* Stay up to date with emerging trends and technologies in data engineering.
* Explore advancements in predictive and prescriptive modelling to drive continuous improvement.