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04/09 PlanetSpark Talent Team
Talent Lead at PlanetSpark

Views:406 Applications:80 Rec. Actions:Recruiter Actions:26

PlanetSpark - Data Engineer (2-5 yrs) Premium

Gurgaon/Gurugram Job Code: 973905

Responsibilities:

- Create and maintain optimal data pipeline architecture

- Assemble large, complex data sets that meet business requirements

- Identifying, designing and implementing internal process improvements including redesigning infrastructure for greater scalability, optimizing data delivery, and automating manual processes

- Work with Data, Analytics & Tech team to extract, arrange and analyse data

- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies

- Building analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition

- Works closely with all business units and engineering teams to develop strategy for long term data platform architecture.

- Working with stakeholders including data, design, product and executive teams and assisting them with data-related technical issues

- Working with stakeholders including the Executive, Product, Data and Design teams to support their data infrastructure needs while assisting with data-related technical issues.

Skill Requirements:

- SQL

- Ruby or Python(Ruby preferred)

- Apache-Hadoop based analytics

- Data warehousing

- Data architecture

- Schema design

- ML

Experience Requirement:

- Prior experience of 2 to 5 years as a Data Engineer.

- Ability in managing and communicating data warehouse plans to internal teams.

- Experience designing, building, and maintaining data processing systems.

- Ability to perform root cause analysis on external and internal processes and data to identify opportunities for improvement and answer questions.

- Excellent analytic skills associated with working on unstructured datasets.

- Ability to build processes that support data transformation, workload management, data structures, dependency and metadata.

Women-friendly workplace:

Maternity and Paternity Benefits

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