BIG Data Solutions Architect

Beschreibung

We are looking for a Big Data Engineer that will work on the collecting, storing, processing, and analyzing of huge sets of data. The primary focus will be on choosing optimal solutions to use for these purposes, then maintaining, implementing, and monitoring them. You will also be responsible for integrating them with the architecture used across our clients.

In this task, you will lead a team of data engineers and data scientists to:

• Assess existing big data ecosystem: architecture, data sourcing, systems integration required to monetise the data
• Understand specific requirements to evolve existing big data ecosystem and perform gap analysis/roadmap definition
• Implement specific big data use-cases by sourcing internal and external data, building data layers, developing analytics algorithms, models and visualisation and overseeing the implementation of 3rdparty tools to monetise data

Specifically:

• Selecting and integrating any Big Data tools and frameworks required to provide requested capabilities
  
• Gather and process raw data at scale (including writing scripts, web scraping, calling APIs, write SQL queries, etc.)
  
• Process unstructured data into a form suitable for analysis and then execute analysis
  
• Support business decisions with ad hoc analysis as needed
  
• Work closely with client engineering team to integrate your innovations and algorithms into production systems
  

Beruflicher Werdegang

• Experience processing large amounts of structured and unstructured data. MapReduce experience is a plus
  
• Proficient understanding of distributed computing principles
  
• Management of Hadoop cluster, with all included services
  
• Proficiency with Hadoop v2, MapReduce, HDFS
  
• Experience with building stream-processing systems, using solutions such as Storm or Spark-Streaming
  
• Good knowledge of Big Data querying tools, such as Pig, Hive, and Impala
  
• Experience with Spark
  
• Experience with integration of data from multiple data sources
  
• Experience with NoSQL databases, such as HBase, Cassandra, MongoDB
  
• Knowledge of various ETL techniques and frameworks, such as Flume
  
• Experience with various messaging systems, such as Kafka or RabbitMQ
  
• Experience with Big Data ML toolkits, such as Mahout, SparkML, or H2O
  
• Good understanding of Lambda Architecture, along with its advantages and drawbacks
  
• Experience with either of Cloudera/MapR/Hortonworks