Data Transformation Manager

*** Discreet Position ***

A global enterprise and a well-known world leader is looking for a Data Transformation Manager to join the Israeli R&D center. The company’s platform incorporates powerful AI based analytic features that serve the most demanding hyper-scale business environments.

Responsibilities

Data transformation manager will own customers’ data transformation processes end to end.

Lead global data engineering teams responsible for the data integration and ETL processes of new and existing customers and will be key part of a successful customer onboarding projects.

Scope of work:

• Lead data onboarding workstreams with new customers / pilots / POC to deliver customer projects in quality and on-time.
• Recruit, establish, train and manage a data engineering team responsible for all Customer and business data, including Data Integrity, Data integrations, Pipelines, Data transformation, Data validation and aggregation, Reports etc.
• Oversee the ETL design & development from various data streams via multiple consumption methods (e.g. Blob, Kafka, SFTP, SQL connectors, REST API).
• Perform data validation activities, normalize and explore datasets, investigate and solve data related problems
• You will define working methodology, KPIs for measuring success, and quality control measures
• Collaborate closely with Core R&D data teams and take an active role in the development lifecycle and design sessions
• Be the data expert focal point for customers and for internal teams such as Technical support, Solutions engineers and business Analysts.

Qualifications

·      BSc in Computer Science or equivalent 

·      7+ years of experience in data engineer, big data

•     3+ years experience as a Data Engineering Manager

·      Hands-on experience in developing and maintaining ETL / ELT - Mandatory

·      Strong SQL with experience in querying large, complex data sets.

Hands-on experience and familiarity with the following tools:

• Big-data query engines (Spark, Databricks)
• Data lakes and data Lakehouse architecture
• Proficiency with SQL and one of Java/Scala
• Python skills, using pandas, numpy
• Familiarity with Azure data – advantage
• Airflow, Docker + K8, Linux/Unix