The Data Engineering role is a key role within the DCI organization. This role focuses on designing, developing, and maintaining scalable data pipelines using cloud infrastructure and agile methodologies, collaborating with cross-functional teams to process large-scale structured and unstructured datasets. The position requires expertise in cloud platforms, data engineering tools, and software engineering practices to build efficient, reusable pipelines that comply with data governance standards and support enterprise-scale reporting and AI solutions. The ideal candidate will work with diverse data sources, automate ETL workflows, utilize containerization and orchestration systems, and stay updated on emerging technologies to continuously improve data pipeline performance and scalability.
Your Role
Key responsibilities in your new role
- Collaborate closely with the reporting team, data scientists, product owners, and domain experts to design, develop, and maintain scalable data pipelines using an agile development methodology.
- Build efficient and reusable pipelines to ingest, process, and manage large-scale structured and unstructured datasets using cloud infrastructure, while ensuring compliance with data governance, lineage, and monitoring standards.
- Work effectively with diverse data sources, including structured datasets (e.g., RDBMS, Hive, Impala) and semi-structured datasets (e.g., JSON, XML, logs).
- Leverage cloud platforms and tools such as AWS, Dataiku, and Hadoop to architect and implement scalable, fault-tolerant data solutions.
- Automate and optimize ETL workflows to streamline data integration and transformation processes for enterprise-scale reporting and AI solutions.
- Develop and maintain a strong foundation in software engineering practices, including CI/CD pipelines, version control tools (e.g., Git), and advanced scripting with Python or PySpark (preferably).
- Utilize containerization and orchestration systems such as Jenkins, Dataiku, and Git to deploy, manage, and monitor scalable data solutions.
- Monitor, troubleshoot, and support operational data pipelines to ensure they meet user requirements, implementing bug fixes and necessary changes promptly.
- Collaborate with cross-functional teams to address requirements for data preparation and processing for advanced analytics, reporting, and machine learning models.
- Stay updated on emerging technologies, tools, and best practices in data engineering to improve data pipeline performance and scalability.
Your Profile
Qualifications And Skills To Help You Succeed
- A Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related field.
- At least 3+ years of hands-on experience in data engineering, with a proven track record of designing and implementing robust data pipelines.
- Proficiency in working with both structured and unstructured data, leveraging databases (e.g., RDBMS, Hive, Impala), APIs and/or NoSQL systems (e.g. MongoDB).
- Strong experience with cloud services, preferably Dataiku, along with tools such as AWS and Hadoop, focused on building scalable and enterprise-grade pipelines.
- Solid expertise in automating and optimizing ETL workflows for both real-time and batch data processing.
- A strong foundation in software/data engineering practices, including CI/CD pipelines, version control tools (e.g., Git,Jenkins), and advanced scripting with Python or PySpark (preferably).
- Familiarity with deployment tools and platforms such as Jenkins to support production-ready pipelines.
- Hands-on experience with big data platforms (e.g., Spark, HDFS, Impala) and a strong understanding of data governance, lineage, and monitoring principles.
- Experience in supporting reporting, visualization, and data analytics tools such as Tableau, and collaborating with data scientists for feature engineering.
- Exceptional collaboration and communication skills, with experience working in cross-functional, agile teams.
- Excellent problem-solving abilities, analytical thinking, and attention to detail.
- A proactive, self-motivated, and results-oriented approach, with the ability to adapt and perform in a dynamic environment.
- Good to have:
- Working knowledge of orchestration systems, including Docker, Kubernetes
- Knowledge of concepts around Data as a product, Data Mesh etc.
- Basics of ML and AI
Contact:
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