āđ€āļ‡āļ·āđˆāļ­āļ™āđ„āļ‚āļāļēāļĢāļ„āđ‰āļ™āļŦāļē

āļ„āđ‰āļ™āļŦāļēāđ‚āļ”āļĒāđƒāļŠāđ‰āļ„āļģāļŦāļĨāļąāļ

āļ„āđ‰āļ™āļŦāļēāđ‚āļ”āļĒāļŦāļĄāļ§āļ”āļŦāļĄāļđāđˆāļ‡āļēāļ™

āđ€āļĨāļ·āļ­āļāļŦāļĄāļ§āļ”āļŦāļĄāļđāđˆāļ‡āļēāļ™

āļ„āđ‰āļ™āļŦāļēāđ‚āļ”āļĒāļ›āļĢāļ°āđ€āļ āļ—āļ˜āļļāļĢāļāļīāļˆ

āđ€āļĨāļ·āļ­āļāļ›āļĢāļ°āđ€āļ āļ—āļ˜āļļāļĢāļāļīāļˆ

āļ„āđ‰āļ™āļŦāļēāđ‚āļ”āļĒāļŠāđˆāļ§āļ‡āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™

āļ•āļąāđ‰āļ‡āđāļ•āđˆ
āļ–āļķāļ‡

āļ„āđ‰āļ™āļŦāļēāđ‚āļ”āļĒāļŠāļ·āđˆāļ­āļšāļĢāļīāļĐāļąāļ—

āļžāļīāļĄāļžāđŒāļŠāļ·āđˆāļ­āļšāļĢāļīāļĐāļąāļ—

āļ„āđ‰āļ™āļŦāļēāđ‚āļ”āļĒāļĢāļ°āļ”āļąāļšāļ•āļģāđāļŦāļ™āđˆāļ‡āļ‡āļēāļ™

āļ„āđ‰āļ™āļŦāļēāđ‚āļ”āļĒāļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™

āđāļŠāļ”āļ‡āļœāļĨ 1 - 7 āļ•āļģāđāļŦāļ™āđˆāļ‡āļ‡āļēāļ™ āļˆāļēāļāļ—āļąāđ‰āļ‡āļŦāļĄāļ” 7 āļ•āļģāđāļŦāļ™āđˆāļ‡āļ‡āļēāļ™
āļ—āļĩāđˆāļĄāļĩāļ„āļģāļ§āđˆāļē Hive
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āļ­āļąāļžāđ‚āļŦāļĨāļ”āđ€āļĢāļ‹āļđāđ€āļĄāđˆāļ‚āļ­āļ‡āļ„āļļāļ“ AI āļ‚āļ­āļ‡āđ€āļĢāļēāļˆāļ°āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđāļĨāļ°āđāļ™āļ°āļ™āļģāļ•āļģāđāļŦāļ™āđˆāļ‡āļ‡āļēāļ™āļ—āļĩāđˆāļ”āļĩāļ—āļĩāđˆāļŠāļļāļ”āđƒāļŦāđ‰āļ„āļļāļ“
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļāļĢāļļāļ‡āđ„āļ—āļĒ 1
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļāļĢāļļāļ‡āđ„āļ—āļĒ 1

āļ—āļąāļāļĐāļ°:

Big Data, Hive, SAS

āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™:

āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™:

āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰

  • Experience migrating from on-premise data stores to cloud solutions.
  • Knowledge of system design and platform thinking to build sustainable solution.
  • Practical experience with modern and traditional Big Data stacks (e.g BigQuery, Spark, Databricks, duckDB, Impala, Hive, etc).
6 āļ§āļąāļ™āļ—āļĩāđˆāļœāđˆāļēāļ™āļĄāļē
āļ”āļđāđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄkeyboard_arrow_down
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļāļĢāļļāļ‡āđ„āļ—āļĒ 2
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļāļĢāļļāļ‡āđ„āļ—āļĒ 2

āļ—āļąāļāļĐāļ°:

Big Data, Hive, SAS

āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™:

āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™:

āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰

  • System Design: Knowledge of system design and platform thinking to build sustainable solutions.
  • Big Data Experience: Practical experience with modern and traditional Big Data stacks (e.g., BigQuery, Spark, Databricks, duckDB, Impala, Hive).
  • Data Warehouse Solutions: Experience working with data warehouse solutions, ELT tools, and techniques (e.g., Airflow, dbt, SAS, Nifi).
6 āļ§āļąāļ™āļ—āļĩāđˆāļœāđˆāļēāļ™āļĄāļē
āļ”āļđāđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄkeyboard_arrow_down
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āđ€āļ­āļīāļ™āļŠāđŒāļ— āđāļ­āļ™āļ”āđŒ āļĒāļąāļ‡ 3
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āđ€āļ­āļīāļ™āļŠāđŒāļ— āđāļ­āļ™āļ”āđŒ āļĒāļąāļ‡ 3

āļ›āļĢāļ°āļŠāļšāļāļēāļĢāļ“āđŒ:

2 āļ›āļĩāļ‚āļķāđ‰āļ™āđ„āļ›

āļ—āļąāļāļĐāļ°:

Big Data, Good Communication Skills, Scala

āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™:

āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™:

āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰

  • You will be involved in all aspects of the project life cycle, including strategy, road-mapping, architecture, implementation and development.
  • You will work with business and technical stakeholders to gather and analyse business requirements to convert them into the technical requirements, specifications, mapping documents.
  • You will collaborate with technical teams, making sure the newly implemented solutions/technology are meeting business requirements.
1 āļ§āļąāļ™āļ—āļĩāđˆāļœāđˆāļēāļ™āļĄāļē
āļ”āļđāđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄkeyboard_arrow_down
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āđ€āļ­āļīāļ™āļŠāđŒāļ— āđāļ­āļ™āļ”āđŒ āļĒāļąāļ‡ 4
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āđ€āļ­āļīāļ™āļŠāđŒāļ— āđāļ­āļ™āļ”āđŒ āļĒāļąāļ‡ 4

āļ›āļĢāļ°āļŠāļšāļāļēāļĢāļ“āđŒ:

6 āļ›āļĩāļ‚āļķāđ‰āļ™āđ„āļ›

āļ—āļąāļāļĐāļ°:

Big Data, Good Communication Skills, Scala

āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™:

āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™:

āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰

  • Collate technical and functional requirements through workshops with senior stakeholders in risk, actuarial, pricing and product teams.
  • Translate business requirements to technical solutions leveraging strong business acumen.
  • Analyse current business practice, processes, and procedures as well as identifying future business opportunities for leveraging Data & Analytics solutions on various platforms.
āļ§āļąāļ™āļ™āļĩāđ‰
āļ”āļđāđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄkeyboard_arrow_down
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āđ„āļ—āļĒāļ­āļ­āļĒāļĨāđŒ 5
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āđ„āļ—āļĒāļ­āļ­āļĒāļĨāđŒ 5
āļĻāļĢāļĩāļĢāļēāļŠāļē, āļŠāļĨāļšāļļāļĢāļĩ, āļšāļĢāļīāļŦāļēāļĢāļœāļĨāļīāļ•āļ āļąāļ“āļ‘āđŒ / āļšāļĢāļīāļŦāļēāļĢāđāļšāļĢāļ™āļ”āđŒāļŠāļīāļ™āļ„āđ‰āļē āļšāļĢāļīāļŦāļēāļĢāļœāļĨāļīāļ•āļ āļąāļ“āļ‘āđŒ / āļšāļĢāļīāļŦāļēāļĢāđāļšāļĢāļ™āļ”āđŒāļŠāļīāļ™āļ„āđ‰āļē

āļ›āļĢāļ°āļŠāļšāļāļēāļĢāļ“āđŒ:

5 āļ›āļĩāļ‚āļķāđ‰āļ™āđ„āļ›

āļ—āļąāļāļĐāļ°:

Data Analysis, Automation, Python

āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™:

āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™:

āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰

  • Work with stakeholders throughout the organization to understand data needs, identify issues or opportunities for leveraging company data to propose solutions for support decision making to drive business solutions.
  • Adopting new technology, techniques, and methods such as machine learning or statistical techniques to produce new solutions to problems.
  • Conducts advanced data analysis and create the appropriate algorithm to solve analytics problems.
3 āļ§āļąāļ™āļ—āļĩāđˆāļœāđˆāļēāļ™āļĄāļē
āļ”āļđāđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄkeyboard_arrow_down
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļšāļīāđŠāļāļ‹āļĩ 6
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļšāļīāđŠāļāļ‹āļĩ 6

āļ—āļąāļāļĐāļ°:

Data Analysis, ETL, Data Warehousing

āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™:

āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™:

āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰

  • Data Architecture: Design, develop, and maintain the overall data architecture and data pipeline systems to ensure efficient data flow and accessibility for analytical purposes.
  • Data Integration: Integrate data from multiple sources, including point-of-sale systems, customer databases, e-commerce platforms, supply chain systems, and other relevant data sources, ensuring data quality and consistency.
  • Data Modeling: Design and implement data models that are optimized for scalability, ...
6 āļ§āļąāļ™āļ—āļĩāđˆāļœāđˆāļēāļ™āļĄāļē
āļ”āļđāđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄkeyboard_arrow_down
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļšāļĢāļīāļĐāļąāļ— āļ•āļīāđŠāļāļ•āđŠāļ­āļ āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļŠāđŒ āļˆāļģāļāļąāļ” 7
āļŦāļēāļ‡āļēāļ™ āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļšāļĢāļīāļĐāļąāļ— āļ•āļīāđŠāļāļ•āđŠāļ­āļ āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļŠāđŒ āļˆāļģāļāļąāļ” 7

āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™:

āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™:

āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰

  • TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
  • Why Join Us.
  • Creation is the core of TikTok's purpose. Our platform is built to help imagination thrive. This is doubly true of the teams that make TikTok possible.
19 āļ§āļąāļ™āļ—āļĩāđˆāļœāđˆāļēāļ™āļĄāļē
āļ”āļđāđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄkeyboard_arrow_down
āļŠāđˆāļ‡āđāļˆāđ‰āļ‡āđ€āļ•āļ·āļ­āļ™āļ‡āļēāļ™āđƒāļŦāļĄāđˆāļĨāđˆāļēāļŠāļļāļ”āļŠāļģāļŦāļĢāļąāļšHive
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āļĨāļ‡āļ—āļ°āđ€āļšāļĩāļĒāļ™āļāļąāļš WorkVenture āđ€āļžāļ·āđˆāļ­āļ„āđ‰āļ™āļŦāļēāļ‡āļēāļ™āđƒāļŦāļĄāđˆāļĨāđˆāļēāļŠāļļāļ”āđāļĨāļ°āļ­āđˆāļēāļ™āļĢāļĩāļ§āļīāļ§āļšāļĢāļīāļĐāļąāļ—āļˆāļēāļāļœāļđāđ‰āļ—āļģāļ‡āļēāļ™āļˆāļĢāļīāļ‡