Create and maintain optimal data pipeline architecture,

Assemble large, complex data sets that meet functional / non-functional business requirements.

Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.

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

Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.

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

Keep our data separated and secure in Azure Data Lake Storage.

Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.

Work with data and analytics experts to strive for greater functionality in our data systems.


Skills required

Experience with object-oriented/object function scripting languages: Python, Scala, etc

Experience in Python programming and libraries like Pandas, NumPy, SciPy

Experience in shell script and simple Linux commands

Strong analytic skills related to working with semi-structured datasets like JSON and XML

Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.

Experience building and optimizing ‘big data’ data pipelines, architectures and data sets.

Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement

Experience in programming using Azure Databricks and ADLS

Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores is added advantage.

Strong hands-on experience with big data tools: Hadoop, Spark, etc.

Experience with relational SQL and NoSQL databases, including Postgres.

Experience with Azure cloud services: ADLS, Key Vaults, ADF, Log Analytics

Knowledge with stream-processing systems: Spark-Streaming.

تاريخ النشر: ٣ يناير ٢٠٢٣
الناشر: Bayt
تاريخ النشر: ٣ يناير ٢٠٢٣
الناشر: Bayt