My client, a well funded services-based start-up, has received a $$ million investment earlier this year, which it is using to scale its team and expand on its already widely-used services platform. In the last year alone, they have seen a huge growth in their user base and, with new funding and hires, look to grow this even further.
- Create and maintain optimal data pipeline architecture
- Identify, analyze, and interpret trends or patterns in complex data sets
- 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 data technologies.
- 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 Executive, Product, Development and Design teams, to assist with data-related technical issues and support their data infrastructure needs.
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL), and 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.
- Experience supporting and working with cross-functional teams in a dynamic environment.
- 5+ years of experience as a Data Engineer
- Experience with big 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 cloud services: EC2, EMR, RDS, Redshift
- Experience with stream-processing systems: Storm, Spark-Streaming, etc.