Data Engineer
What You’ll Do:
- Build & deploy large-scale ETL and stream processing pipelines in our serverless microservice infrastructure built on top of industry standard technology (Kubernetes & Kafka)
- Manage workflows in support of both product and our AI/Data Science pipeline, you’ll be introduced to our unique ingest and processing pipelines turning proprietary data assets into ground breaking solutions
- Build stream ingestion processes to efficiently send, process, analyze & publish data
- Perform analyses of large structured and unstructured data to solve multiple & complex business problems
- Investigate and prototype different task dependency frameworks to understand the most appropriate design for a given use case
- Work hand-in-hand with the data science team to understand various user or content trends that influence product changes and customer acquisition strategies
Who You Are:
- An Engineer interested in working in both streaming and batch processing environments [Spark, Kafka streaming, Kinesis)
- A tech-enthusiast excited to work with Cloud Based Technologies (GCP, Azure, AWS)
- A doer who loves to produce meaningful analytic insights for an innovative, data-intensive products
- Always curious about analytics frameworks and you are well-versed in the advantages and limitations of various big data architectures and technologies
- Believer in transparency & communication
- Coding skills for analytics and data engineering/manipulation (Scala, Java and Python)
- Experience with SQL & NoSQL database systems, S3 & distributed big data technologies including Hadoop and Spark. Knowledge /awareness of orchestration systems like Airflow, NiFi, Pentaho a plus but not required
The Tools We Use:
Tools can be learned, so please don’t shy away from applying if you’re a strong engineer! To give you a flavor of our current tools:
- Language: Scala/Java/Python Streaming: Spark Streaming, Kafka
- Cloud Technologies: GCP (BigQuery, Cloudproc, Cloudflow, BigQuery, Compute Engine), Azure (HDInsight, data factory)