Senior Data Engineer
Procter & Gamble is hiring a Senior Data Engineer. Level rates it ; you can apply on Procter & Gamble.
Level's read on this job
- Level rates this data engineer role Little AI ●○○○: aI is not part of the work.
- The posting mentions Databricks, Azure and Azure Data Factory among its tools and skills.
- Level classes it as a senior role.
- Procter & Gamble has 873 other open roles on Level.
AI in this role
Senior Data Engineer to lead the design and development of cloud-based data and analytics platforms using Azure and Databricks.
Job Location
HYDERABAD OFFICE APAC PSC GDOPJob Description
Job DescriptionCF-IT's TOP organization is seeking a Senior Data Engineer, located at the Hyderabad IT Hub. Specifically, you'll work within the Data & AI section that supports the Modern IT Service & Asset Management team.
Responsibilities:
Leading design and development of data and analytics cloud-based platform. Crafting integrated systems, implementing ELT/ ETL jobs to fulfil business deliverables. Performing sophisticated data operations such as data orchestration, transformation, and visualization with large datasets. You will be working with product managers to ensure superior product delivery to drive business value & transformation. Demonstrating standard coding practices to ensure delivery excellence and reusability.
Data Ingestion: Develop and maintain data pipelines to extract data from various sources and load it into Azure and Databricks environments.
Data Transformation: Design and implement data transformation processes, including data cleansing, normalization, and aggregation, to ensure data quality and consistency.
Data Modeling: Develop and maintain data models and schemas to support efficient data storage and retrieval in Azure and Databricks platforms.
Data Warehousing: Design and build data warehouses or data lakes using Azure services such as Azure Data Lake Storage, Databricks DeltaLake
Data Integration: Integrate data from multiple sources, both on-premises and cloud-based, using Azure Data Factory or other relevant tools.
Data Governance: Implement data governance practices, including data security, privacy, and compliance, to ensure data integrity and regulatory compliance.
Performance Optimization: Optimize data pipelines and queries for improved performance and scalability in Azure and Databricks environments.
Monitoring and Troubleshooting: Monitor data pipelines, identify and resolve performance issues, and troubleshoot data-related problems in collaboration with other teams.
Data Visualization: Build BI reports to enable faster decision making.
Collaboration: Work with product managers to ensure superior product delivery to drive business value & transformation
Documentation: Document data engineering processes, data flows, and system configurations for future reference and knowledge sharing.
Job Qualifications
Experience: Bachelor's or master's degree in computer science, data engineering, or a related field, along with 4+ year work experience in data engineering and cloud platforms.
Azure and Databricks: Strong proficiency in Azure services such as Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Data Lake Storage, and Azure Synapse Analytics.
ETL Tools: Experience with ETL (Extract, Transform, Load) tools and frameworks, such as Apache Spark, Databricks Delta, or Azure Data Factory, for data integration and transformation.
Programming: Proficiency in programming languages such as PySpark, Python, SQL, or Scala for data manipulation, scripting, and automation.
Data Modeling: Knowledge of data modeling techniques and experience with data modeling tools.
Database Technologies: Familiarity with relational databases (e.g., SQL Server) for data storage and retrieval.
Data Warehousing: Understanding of data warehousing concepts, dimensional modeling, and experience with data warehousing technologies such as Azure Synapse Analytics or Azure SQL Data Warehouse or Azure Databricks DeltaLake
Data Governance: Knowledge of data governance principles, data security, privacy regulations, and experience implementing data governance practices.
Data Visualization: Experience of working with Microsoft Power BI to build semantic data model & BI reports/dashboards.
Cloud Computing: Familiarity with cloud computing concepts and experience working with cloud platforms, particularly Microsoft Azure.
Problem-Solving: Strong analytical and problem-solving skills to identify and resolve data-related issues.
Proficiency in DevOps Tools and CICD tools (e.g. Azure DevOps, Chef, Puppet, Github)
Job Schedule
Full timeJob Number
R000159103Job Segmentation
Experienced ProfessionalsHow we rate this
Senior Data Engineer at Procter & Gamble rates 30 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data engineer jobs at this level
Pay
- Typical data engineer job
- $146k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
Questions you could be asked
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where data warehousing was part of your work. What did you do?
- Tell me about a project where data modeling was part of your work. What did you do?
- Tell me about a project where data governance was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Data Engineering, ETL, Data Warehousing, Data Modeling, and Data Governance. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
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