EY - GDS Consulting - AI And DATA - Data Engineering Manager

10 - 15 years

40 - 45 Lacs

pune

Posted:6 days ago| Platform:

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Skills Required

hive python data analysis data management data analytics data warehousing data architecture machine learning data engineering artificial intelligence sql data bricks tableau data modeling spark predictive modeling hadoop aws etl big data

Work Mode

Work from Office

Job Type

Full Time

Job Description

The Data Engineering Manager leads large scale solution architecture design and optimisation to provide streamlined insights to partners throughout the business. This individual leads the team of Mid and Senior data engineers to partner with visualization on data quality and troubleshooting needs.
The Data Engineering manager will
- Implement data processes for the data warehouse and internal systems
- Lead a team of Junior and Senior Data Engineers in executing data processes and providing quality, timely data management
- Managing data architecture, designing ETL process
- Clean, aggregate and organize data from disparate sources and transfer it to data warehouses.
- Lead development testing and maintenance of data pipelines and platforms, to enable data quality to be utilized within business dashboards and tools.
- Support team members and direct reports in refining and validating data sets.
- Ensure we follow all the enterprise data management and principles. Understanding data management best practices
- Create, maintain, and support the data platform and infrastructure that enables the analytics front-end; this includes the testing, maintenance, construction, and development of architectures such as high-volume, large-scale data processing and databases with proper verification and validation processes.
- Enterprise data governance principles - All the projects that are coming are following the process. Data catalogue, data security, access policy control, data lineage, new data domains
- Understanding of data domains - Pharma commercials, medical affairs, sales and marketing, patient services

Your key responsibilities

Lead the design, development, optimization, and maintenance of data architecture and pipelines adhering to ETL principles and business goals.
Develop and maintain scalable data pipelines, build out new integrations using AWS native technologies and data bricks to support increases in data source, volume, and complexity.
Define data requirements, gather and mine large scale of structured and unstructured data, and validate data by running various data tools in the Big Data Environment.
Lead the ad hoc data analysis, support standardization, customization and develop the mechanisms to ingest, analyze, validate, normalize, and clean data.
Write unit/integration/performance test scripts and perform data analysis required to troubleshoot data related issues.
Implement processes and systems to drive data reconciliation and monitor data quality, ensuring production data is always accurate and available for key stakeholders, downstream systems, and business processes.
Lead the evaluation, implementation and deployment of emerging tools and processes for analytic data engineering to improve productivity.
Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes.
Solve complex data problems to deliver insights that help achieve business objectives.
Partner with Business Analysts and Enterprise Architects to develop technical architectures for strategic enterprise projects and initiatives.
Coordinate with Data Scientists, visualization developers and other data consumers to understand data requirements, and design solutions that enable advanced analytics, machine learning, and predictive modelling.
Collaborate with AI/ML engineers to create data products for analytics and data scientist team members to improve productivity.
Advise, consult, mentor and coach other data and analytic professionals on data standards and practices, promoting the values of learning and growth.
Foster a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytical solutions.

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