Job Title: Data Warehouse Engineer
Reports To: Chief Decision Scientist
Supervises: Data Engineer
In this role, you will have the capacity to truly move the needle of success, working closely with the Chief Decision Scientist and the rest of the organization, to implement real company-wide change and innovation.
Data Warehouse Engineer will be essential in driving the data engineering efforts for Renmoney. They will work closely with Decision Science team to build and maintain products that help leverage data to improve bottom-line business metrics.
Required Qualifications and Experience
Bachelor's degree in Computer Science or related technical field, or equivalent practical experience
5+ years of experience working with one or more of the following: PySpark, SQL, Java, R, or similar language, Unix/Linux systems
3+ years of experience with ETL and AWS
Scripting experience in Shell, Perl or Python
Skills and Competency Requirements
Great collaborative skills to work with different teams and departments.
Strong coding skills especially in scripting languages like Python (PySpark).
Hands on experience with AWS Glue and Athena
Expertise in writing performant SQL queries.
Deep familiarity and comfortable with working in the Linux environment.
Experience in working with both OLTP (Postgres/Mysql) and MPP OLAP (Redshift/Vertica/Snowflake) databases.
Experience processing data with SQL, building ETLs and data pipelines, and deploying them into production.
Experience managing and maintaining an AWS data warehouse
Key Responsibilities and Duties
Help maintain and extend data warehousing capabilities.
Define each data label and create appropriate description and data types (general data profiling)
Improve functionality of existing machine learning infrastructure.
Build new ETLs and extend existing ones.
Own the architecture, delivery, and evolution of interrelated big data systems
Design and build scalable, efficient, and reliable data pipelines.
Build and maintain Semantic Layer for Decisions
Build and maintain data and reporting solutions.
Extend instrumentation for better event logging capabilities.
Work closely with different business units to encode business logic into code.
Do intermittent data related work
Collaborating with cross functional teams comprised of technical and business colleagues
Participate in and help guide research, including design, coding, and performance measures