Vacancy expired!
- Create and maintain optimal data pipeline architecture,
- Assemble large, complex data sets that 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.
- 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 customer acquisition, operational eciency and other key business performance metrics.
- Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
- 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 working with distributed data technologies (e.g. Hadoop, MapReduce, Spark, Kaa, Flink etc) for building ecient, large-scale ‘big data’ pipelines.
- Strong Software Engineering experience with prociency in at least one of the following programming languages: Java, Python, Scala or equivalent.
- Implement data ingestion pipelines both real time and batch using best practices.
- Experience with building stream-processing applications using Apache Flink, Kaa Streams or others.
- Experience with Cloud Computing platforms like Amazon AWS, Google Cloud etc.
- Build processes supporting data transformation, data structures, metadata, dependency and workload management.
- Experience supporting and working with cross-functional teams in a dynamic environment.
- Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
- Ability to work in a Linux environment.
- Experience in building distributed, high-volume data services.
- Experience with big data processing and analytics stack in AWS: EMR, S3, EC2, Athena, Kinesis, Lambda, Quicksight etc.
- Knowledge of data science tools and their integration with data lakes.
- Experience in container technologies like Docker/Kubernetes
- S .in computer science, software engineering, computer engineering, electrical engineering, or related area of study.
Vacancy expired!