am - data engineer in bengaluru / bangalore

randstad india
position type
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bengaluru / bangalore
Information Technology
position type
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randstad india

job description

am - data engineer in bengaluru / bangalore

Ability to handle large amount of complex client data and data structures
 Key work will be data preparation for model building , reporting, portfolio management, etc
 Good understanding of P&C data and across functions – Claims, policy, UW, finance, CAT etc
 Experience in designing and implementation of ETL function(Extraction, transformation and
 Experience with data modelling, Design pattern, building highly scalable and secured
analytics solutions
 Ensures Data Accuracy & Flexibility
 Building Pipelines for various ETL operations
 Find trends in data sets and developing algorithms to help make raw data more useful to the
 Develop, construct, test and maintain architectures
 Align architecture with business requirements
 Data acquisition
 Develop data set processes
 Use programming language and tools
 Identify ways to improve data reliability, efficiency and quality
 Conduct research for industry and business questions
 Use large data sets to address business issues
 Deploy sophisticated analytics programs, machine learning and statistical methods
 Prepare data for predictive and prescriptive modelling
 Find hidden patterns using data
 Use data to discover tasks that can be automated
 Deliver updates to stakeholders based on analyticsQualifications
 Graduate / Masters with min 5 years of relevant work experience
 Strong knowledge of MSBI stack (SSIS, SSAS, SQL)
 Experience in programming with Python, processing data in an efficient way
 Coding: Proficiency in coding languages is essential to this role, so consider taking courses to
learn and practice your skills. Common programming languages includeSQL, NoSQL, Python,
Java, R, and Scala. Relational and non-relational databases: Databases rank among the most common
solutions for data storage. You should be familiar with both relational and non-relational
databases, and how they work.
 ETL (extract, transform, and load) systems: ETL is the process by which you’ll move data
from databases and other sources into a single repository, like a data warehouse. Common
ETL tools include Xplenty, Stitch, Alooma, and Talend.
 Data storage: Not all types of data should be stored the same way, especially when it comes
to big data. As you design data solutions for a company, you’ll want to know when to use a
data lake versus a data warehouse, for example.
 Automation and scripting. Automation is a necessary part of working with big data simply
because organizations are able to collect so much information. You should be able to write
scripts to automate repetitive tasks.
 Machine learning: While machine learning is more the concern of data scientists, it can be
helpful to have a grasp of the basic concepts to better understand the needs of data
scientists on your team.


AWS, data engineer