To be successful as a Data Engineer you should have experience with:
- Strong experience with ETL tools such as Ab Initio, Glue, PySpark, Python, DBT, DataBricks and various AWS required services / products.
- Advanced SQL knowledge across multiple database platforms (Teradata , Hadoop, SQL etc.)
- Experience with data warehousing concepts and dimensional modeling.
- Proficiency in scripting languages (Python, Perl, Shell scripting) for automation.
- Knowledge of big data technologies (Hadoop, Spark, Hive) is highly desirable.
- Bachelor's degree in Computer Science, Information Systems, or related field.
- Experience in ETL development and data integration.
- Proven track record of implementing complex ETL solutions in enterprise environments.
- Experience with data quality monitoring and implementing data governance practices.
- Knowledge of cloud data platforms (AWS, Azure, GCP) and their ETL services.
Some other highly valued skills include:
- Strong analytical and problem-solving skills.
- Ability to work with large and complex datasets.
- Excellent documentation skills.
- Attention to detail and commitment to data quality.
- Ability to work independently and as part of a team.
- Strong communication skills to explain technical concepts to non-technical stakeholders.
You may be assessed on key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen, strategic thinking and digital and technology, as well as job-specific technical skills.
This role is based in Pune.
Purpose of the role
To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure.
Accountabilities
- Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
- Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
- Development of processing and analysis algorithms fit for the intended data complexity and volumes.
- Collaboration with data scientist to build and deploy machine learning models.