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Data Engineer

Find Human Resources Philippines Posted: 4 Month Work From Home
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Data Engineer 

As a Data Engineer, you will be instrumental in helping our customers get insights and value from their data by designing, building, implementing and managing the technical solution and data platform that will enable the delivery and consumption of data. You will have a good understanding of the customer's requirements, needs and business objectives, and be able to translate them into technical data solutions. Not only will you be responsible in ingesting, transforming, cleansing, harmonizing, preparing and structuring data for consumption, you will also help advise on potential ways this can be done better and more efficiently, and leverage on best practices and collective experience to guide our customers to explore options they may not even raise as a requirement but we know will help them in their data and analytics journey.

Job Description:

· Designs and develops Data and Analytics solutions

· Performs data analysis, data architecture, data engineering activities

· Designs and builds data ingestion pipelines; develops data transformation, cleansing and preparation processes

· Designs and builds data storage solutions including database objects and data structures

· manner

· Provides advisory and consultation services to determine the right solution

· Utilizes best practices, work plans, checklists and defined processes

· Proactively supports application teams

· Performs optimization

· Participates in project technical reviews

· Leads or participates in work product reviews

· Engages SME or vendors to provide assistance to resolve more complex issues as required

Technology and Functional Skills:

· Azure data platform and components: Azure Data Factory, Databricks, Blob Storage, Synapse, SQL DB/DW, Event Hubs, etc

· AWS data platform and components: S3, Redshift, Spectrum, Athena, Aurora, CLI, etc

· GCP data platform and components: BigQuery, Dataflow, Dataprep, Pub/Sub, etc

· Big Data: Hadoop, Hadoop ecosystem, Spark, Scala, etc.

· Language and Toolset: SQL, Python, PySpark, ELK (Elasticsearch, Logstash, Kibana)

· Data Integration, Extract-Transform-Load (ETL), Data Ingestions, Data Cleansing, Data Preparation, Database

· Data Warehouse, Data Lake, and Business Intelligence (BI) concepts

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