What you will be doing:
- 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 analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
- Work with stakeholders including the Product, Data, Engineering and business teams to assist with data-related technical issues and support their data infrastructure needs.
- Maintain data security and integrity across geographical boundaries as the organization scales.
- 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.
What we'd like to see in the candidate:
- 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 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.
- Experience supporting and working with cross-functional teams in a dynamic environment.
- We are looking for a candidate with 3+ years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools:
- Experience with common, open-source data tools: Hadoop, Spark, Kafka, etc.
- Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
- Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
- Experience with AWS data stack: EC2, EMR, RDS, Redshift, Athena, Firehose, Glue, or
- Experience with GCP data stack: data storage services, DataProc, DataFlow, BigQuery, PubSub
- Experience with at least two of object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
- Experience and passion for Agile software processes, site reliability principles, rapid prototyping and responsible experimentation
- Experience in building machine learning platforms
- and pipelines for training and running machine learning models on distributed systems