About the role
- Development,
maintenance, and enhancement of Data Pipelines (ETL/ELT) and processes
with thorough knowledge of star/snowflake schemas
- Hands
on experience in PowerCenter Developer/IDMC/IICS Data Integration
- Experience
in DW Production Support / troubleshooting data or pipeline issues
- Developing
complex SQL queries and SQL optimization
- Development
experience must be full Life Cycle experience including business
requirements gathering, data sourcing, testing/data reconciliation, and
deployment within Business Intelligence/Data Warehousing Architecture.
- Designing
and implementing data security
- Monitoring
and optimizing data storage and data processing
- Use of
Object function/object-oriented scripting languages including Scala, C++,
Java and Python
- Cloud
data warehouse and data lake platforms (e.g., Snowflake, Databricks,
Redshift, BigQuery, MS Synapse, MS ADLS, Apache Haddop, etc.)
- Working knowledge of message queuing, stream processing, and highly
scalable ‘big data’ data stores.
- Strong
project management and organizational skills.
- Experience
supporting and working with cross-functional teams in a dynamic
environment.
- Understanding
of data science concepts
- Familiarity
with AI/ML frameworks and libraries
Requirements
•
Developing data services that are fit for purpose, resilient, scalable, and
future proof, to meet user needs
•
Advanced SQL knowledge and experience working with relational databases and
working familiarity with various databases.
•
Demonstrated understanding and experience using software and tools including
ETL, relational SQL and NoSQL databases, big data tools like Kafka, Spark, and
Hadoop
•
Experience building and optimizing ‘big data’ data pipelines, architecture, and
data sets.
•
Experience performing root cause analysis on internal and external data and
processes to answer specific business questions and identify opportunities for
improvement.
•
Strong analytic skills related to working with unstructured datasets.
•
Experience and knowledge of project management best practices and agile
software development methodologies
•
A successful history of manipulating, processing, and extracting value from
large, disconnected datasets.
•
Working knowledge of message queuing, stream processing, and highly scalable
‘big data’ data stores.
•
Strong project management and organizational skills.
•
Experience supporting and working with cross-functional teams in a dynamic
environment.
•
Understanding of data science concepts
•
Familiarity with AI/ML frameworks and libraries