*Big Data Engineer - London - £80K - Online Platform*
|Job Title:||*Big Data Engineer - London - £80K - Online Platform*|
|Location:||City of London, London|
|Salary:||£50000 - £80000 per annum|
|Contact Name:||Peter Tam|
|Job Published:||12 months ago|
An established Online/ media company is looking to expand its data engineering function by adding a experienced Developer to their ranks.
This award winning data team are looking on increasing data automation by designing and building advanced data products, recommendation engines, personalised customer targeting and optimisation.
Do you really like to explore large amounts of data? Have a keen interested in Hadoop-based products? Other tools such as Spark, Hive, Pig, Cassandra, Elastic Search?
The current team work in an agile way, open and collaborative communication to improve functionality. Reporting to the head of Big Data Data Science, this role will be to build personalised models using unstructured raw customer data, and making a story with the data for business insight.
To be part of a stronghold of data engineer, Dev Ops, data scientists and excellent PhD established professionals at the peak of their career.
- Experienced with/and Hadoop, Kafka, Spark, Pig, Hive etc.
- Good experience with Java, Scala, Python coding
- Experience with working with real-time applications (desirable)
- Working closely with the data science team
- Exposure to: end to end Big Data implementation
- Good understanding of Big Data Eco system and data architecture
- ETL, Data Quality, Data Management
- SQL and Python
- Excellent communication skills - good level of stakeholder management (business acumen)
- Competitive Salary
- Collaborative environment
- Working with data leaders
- Interesting and varied projects
*Unfortunately Sponsorship is not provided.
Parallel Consulting is a multi-awarding winning, global leader in Analytics & Data Science recruitment.
With over 12 years expertise, we assist our clients within Analytics, Customer & Marketing Insight, Web Analytics, Big Data, Data Science, Credit Risk and BI.
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