Data Lake Engineer, AVP

As a Data Lake Engineer at Deutsche Bank, you'll be responsible for designing, building, and maintaining the data storage systems within a scalable cloud environment. Key duties include creating efficient data pipelines for data ingestion and transformation, ensuring data quality and reliability, and collaborating with business analysts and data scientists to understand their data needs. You will also implement automation scripts and contribute to change management processes to enhance operational efficiency.

The Data Lake Engineer role at Deutsche Bank requires proficiency in programming languages such as Python, SQL, or Scala, along with experience in database technologies including relational and NoSQL databases. Familiarity with cloud platforms like GCP, AWS, or Azure, as well as experience with data pipelines and ETL tools, is essential. A strong understanding of infrastructure as code (IaC) and automation tools like Terraform is also crucial for this position.

To be considered for the Data Lake Engineer position at Deutsche Bank, candidates typically need a bachelor's degree in computer science, information technology, or a related field. Additionally, obtaining data engineering certifications such as IBM Certified Data Engineer or Google’s Certified Professional can enhance your application and demonstrate your expertise in the field.

Deutsche Bank is committed to supporting the career development of its Data Lake Engineers. You will benefit from continuous training and learning opportunities, coaching from industry experts, and a culture that encourages growth and development. The bank also offers a variety of flexible benefits tailored to your needs, helping you to excel in your career.

Deutsche Bank offers a comprehensive benefits package for its employees in Pune, including a best-in-class leave policy, 100% reimbursement under childcare assistance, and gender-neutral parental leaves. Employees also enjoy comprehensive hospitalization insurance for themselves and their dependents, accident and term life insurance, and complimentary health screenings for those 35 years and older, ensuring a supportive work environment.

When answering this question, highlight any hands-on experience you have with cloud platforms such as GCP, AWS, or Azure. Discuss specific projects you've worked on, the challenges faced, and how you leveraged cloud services to design scalable data solutions. Emphasize your familiarity with the platform's services and your ability to optimize performance and security.

To answer this question, focus on the methods you employ to maintain data integrity, such as implementing automated validation checks, using data profiling techniques, and establishing a data governance framework. Share examples of how you've identified data quality issues in the past and the strategies you implemented to resolve them, emphasizing your proactive approach.

Discuss your proficiency in languages like Python, SQL, or Scala, providing concrete examples of how you've utilized these languages for data manipulation, pipeline development, or automation tasks. Mention specific frameworks or libraries you've worked with and how they contributed to the success of your projects.

Describe your experience in designing and implementing ETL processes, emphasizing tools and frameworks you've used like Apache NiFi, Talend, or custom scripts. Provide examples of data sources you've worked with and the transformations you applied to meet business needs. Highlight any optimizations you've made to improve performance in data ingestion.

Emphasize the importance of clear communication and understanding the specific data requirements of both data scientists and business analysts. Share examples of how you've successfully collaborated on projects to deliver actionable insights, illustrating your ability to balance technical knowledge with business requirements.

When discussing IaC, explain its significance in maintaining and provisioning infrastructure through code rather than manual processes. Highlight specific tools you've used, such as Terraform or CloudFormation, and explain how IaC contributed to consistency and reproducibility in your data engineering work.

Detail your experience with database optimization techniques, such as indexing, partitioning, or using caching strategies. Discuss your ability to monitor performance and how you've proactively tackled bottlenecks in data retrieval, enhancing overall application efficiency.

Share your approach to project management, including any methodologies you follow, such as Agile or Scrum. Discuss tools you've used for task management and how you prioritize tasks based on deadlines, project goals, and stakeholder needs, illustrating your ability to adapt to changing circumstances.

Discuss current trends such as the rise of data mesh architecture, increased adoption of cloud-native data warehouses, or advancements in real-time data processing. Share your insights on how these trends affect data engineering practices and how you are staying updated on these developments to remain competitive in the field.

When addressing this question, express your understanding of the evolving role of data lakes. Mention trends like increased integration with machine learning, the shift towards hybrid data architectures, and the growing emphasis on data governance. Articulate your vision on how data lakes will adapt to meet future data needs in organizations and your role in that future.