The Data Engineer is responsible for expanding and optimizing data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. The Data Engineer will support our data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. The right candidate will be excited by the prospect of optimizing or even re-designing our company's data architecture to support our next generation of products and data initiatives.
ESSENTIAL DUTIES AND RESPONSIBILITIES
Create and maintain optimal data pipeline architecture,
Assemble structured, semi-structure and unstructured data sets to meet functional / non-functional business requirements.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. Familiarity DataOps and/or DevOps.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS 'big data' technologies.
Build analytics tools that utilize the data pipeline to provide actionable insights into market insights and transparency, operational efficiency and other key business performance metrics.
Work with stakeholders including the Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
Keep our data separated and secure across regional boundaries through multiple data centers and AWS regions. Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
Work with data and analytics experts to strive for greater functionality in our data systems.
5 years of experience in a Data Engineer role. Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
Experience building and optimizing 'big data' data pipelines, architectures 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.
Build processes supporting data transformation, data structures, metadata, dependency and workload management.
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.
Experience with AWS cloud services: S3, Athena, Aurora, RDS, Redshift, Glue, Lambda, EventBridge, SQS, SNS
Experience with relational SQL and NoSQL databases, including Postgres and DynamoDB.
Experience with scripting languages: Python, R, etc.
Experience in AWS Technologies like EMR, S3, Batch ingestion
Experience in debugging and performance tuning of spark jobs. Should have experience working with multiple file formats especially with Parquet and Avro
Experience implementing Hybrid/Multi Data Warehousing
Experience in microservices APIs is needed to perform some duties