Job Summary
We are seeking a highly experienced and results-driven Senior ETL Developer with over 6 years of professional experience in data integration, transformation, and analytics across enterprise-grade data platforms. This role requires deep expertise in ETL development, strong familiarity with cloud-based data solutions, and the ability to manage large-scale data operations. The candidate should be capable of working across complex data environments, including structured and unstructured datasets, and demonstrate fluency in handling both traditional and modern cloud data ecosystems. The ideal candidate must have strong hands-on experience with ETL tools, advanced SQL and Python scripting, big data processing, and cloud-based data services, particularly within the AWS ecosystem.This position will play a key role in the design, development, and optimization of scalable data pipelines and contribute to enterprise-level data engineering solutions, while supporting analytical and reporting needs in both Application Development (AD) and Application Maintenance Support (AMS) environments.
Key Responsibilities
- Design, develop, and maintain efficient and scalable ETL pipelines using modern data tools and platforms, focusing on extraction, transformation, and loading of large datasets from multiple sources.
- Work closely with data architects, analysts, and other stakeholders to understand business data requirements and translate them into robust technical ETL solutions.
- Implement and optimize data loading, transformation, cleansing, and integration strategies to ensure high performance and quality in downstream applications.
- Develop and manage cloud-based data platforms, particularly within the AWS ecosystem, including services such as Amazon S3, EMR, MSK, and SageMaker.
- Collaborate with cross-functional teams to integrate data from various databases such as Snowflake, Oracle, Amazon RDS (Aurora, Postgres), DB2, SQL Server, and Cassandra.
- Employ scripting languages like SQL, PL/SQL, Python, and Unix shell commands to automate data transformations and monitoring processes.
- Leverage big data technologies such as Apache Spark and Sqoop to handle large-scale data workloads and enhance data processing capabilities.
- Support and contribute to data modeling initiatives using tools like Erwin and Oracle Data Modeler; exposure to Archimate will be considered an advantage.
- Work with scheduling and orchestration tools including Autosys, SFTP, and preferably Apache Airflow to manage ETL workflows efficiently.
- Troubleshoot and resolve data inconsistencies, data load failures, and performance issues across the data pipeline and cloud infrastructure.
- Follow best practices in data governance, metadata management, version control, and data quality frameworks to ensure compliance and consistency.
- Maintain documentation of ETL processes, data flows, and integration points for knowledge sharing and auditing purposes.
- Participate in code reviews, knowledge transfer sessions, and mentoring junior developers in ETL practices and cloud integrations.
- Stay up to date with evolving technologies and trends in data engineering, cloud services, and big data to proactively propose Technical Skills : ETL Tools : Experience with Talend is preferred (especially in AD and AMS functions), although it may be phased out in the Databases : Expertise in Snowflake, Oracle, Amazon RDS (Aurora, Postgres), DB2, SQL Server, and Cassandra.
- Big Data & Cloud : Hands-on with Apache Sqoop, AWS S3, Hue, AWS CLI, Amazon EMR, Amazon MSK, Amazon SageMaker, Apache Scripting : Strong skills in SQL, PL/SQL, Python; knowledge of Unix command-line is essential; R programming is optional but considered a Scheduling Tools : Working knowledge of Autosys, SFTP, and preferably Apache Airflow (training can be Data Modeling Tools : Proficiency in Erwin, Oracle Data Modeler; familiarity with Archimate is a preferred Notes :
Power BI knowledge is relevant only in shared AD roles and not required for dedicated ETL and AWS roles or AMS responsibilities.
The role requires strong communication skills to collaborate with technical and non-technical stakeholders, as well as a proactive mindset to identify and resolve data challenges.Must demonstrate the ability to adapt in fast-paced and changing environments while maintaining attention to detail and delivery quality.Exposure to enterprise data warehouse modernization, cloud migration projects, or real-time streaming data pipelines is considered highly advantageous.(ref:hirist.tech)
