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We are looking for an experienced Data Engineer to join our growing team of analytics. The candidate will be responsible for expanding and optimizing our data and data pipeline, as well as optimizing data flow. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. Candidate must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. Responsibilities
Create and maintain optimal data pipeline.
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 Jobs/code for greater scalability, etc.
Work with stakeholders including the Product owner, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
Work with data and analytics experts to strive for greater functionality in our data systems.
Advanced working SQL knowledge and experience working with RDBMS, Hadoop and NoSQL DB.
Experience building and optimizing 'big data' data pipelines, Jobs 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 structured and 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 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 5+ 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:
Big data tools: Hadoop, Spark, Kafka, etc.
Relational SQL and NoSQL databases, including Postgres and Cassandra.
Data pipeline and workflow management tools: Airflow, etc.
AWS cloud services or GCP.
Stream-processing systems: Spark-Streaming, Flink etc.