Data Engineer
Series 'A' funded Silicon Valley based BI startup
Data Engineer at Series 'A' funded Silicon Valley based BI startup · Bengaluru (Bangalore) · 4 - 6 years · ₹30L - ₹45L / yr · Posted 18 May 2021

4 - 6 yrs
₹30L - ₹45L / yr
Bengaluru (Bangalore)
Skills
Data engineering
Data Engineer
Data Warehouse (DWH)
Big Data
Spark
SQL
Apache Spark
ETL
Linux/Unix
It is the leader in capturing technographics-powered buying intent, helps
companies uncover the 3% of active buyers in their target market. It evaluates
over 100 billion data points and analyzes factors such as buyer journeys, technology
adoption patterns, and other digital footprints to deliver market & sales intelligence.
Its customers have access to the buying patterns and contact information of
more than 17 million companies and 70 million decision makers across the world.
Role – Data Engineer
Responsibilities
Work in collaboration with the application team and integration team to
design, create, and maintain optimal data pipeline architecture and data
structures for Data Lake/Data Warehouse.
Work with stakeholders including the Sales, Product, and Customer Support
teams to assist with data-related technical issues and support their data
analytics needs.
Assemble large, complex data sets from third-party vendors to meet 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, Elasticsearch,
MongoDB, and AWS technology.
Streamline existing and introduce enhanced reporting and analysis solutions
that leverage complex data sources derived from multiple internal systems.
Requirements
5+ years of experience in a Data Engineer role.
Proficiency in Linux.
Must have SQL knowledge and experience working with relational databases,
query authoring (SQL) as well as familiarity with databases including Mysql,
Mongo, Cassandra, and Athena.
Must have experience with Python/Scala.
Must have experience with Big Data technologies like Apache Spark.
Must have experience with Apache Airflow.
Experience with data pipeline and ETL tools like AWS Glue.
Experience working with AWS cloud services: EC2, S3, RDS, Redshift.
companies uncover the 3% of active buyers in their target market. It evaluates
over 100 billion data points and analyzes factors such as buyer journeys, technology
adoption patterns, and other digital footprints to deliver market & sales intelligence.
Its customers have access to the buying patterns and contact information of
more than 17 million companies and 70 million decision makers across the world.
Role – Data Engineer
Responsibilities
Work in collaboration with the application team and integration team to
design, create, and maintain optimal data pipeline architecture and data
structures for Data Lake/Data Warehouse.
Work with stakeholders including the Sales, Product, and Customer Support
teams to assist with data-related technical issues and support their data
analytics needs.
Assemble large, complex data sets from third-party vendors to meet 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, Elasticsearch,
MongoDB, and AWS technology.
Streamline existing and introduce enhanced reporting and analysis solutions
that leverage complex data sources derived from multiple internal systems.
Requirements
5+ years of experience in a Data Engineer role.
Proficiency in Linux.
Must have SQL knowledge and experience working with relational databases,
query authoring (SQL) as well as familiarity with databases including Mysql,
Mongo, Cassandra, and Athena.
Must have experience with Python/Scala.
Must have experience with Big Data technologies like Apache Spark.
Must have experience with Apache Airflow.
Experience with data pipeline and ETL tools like AWS Glue.
Experience working with AWS cloud services: EC2, S3, RDS, Redshift.
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