Data Engineer
4 weeks ago
Responsibilities :
- Develop, construct, test, and maintain architectures such as large-scale processing systems, and other infrastructure using tools and technologies such as Hadoop, Spark, and Kafka.
- Assemble large, complex data sets that meet functional / non-functional business requirements, utilizing SQL, NoSQL, and cloud-based data warehousing solutions such as Amazon Redshift or Google BigQuery.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc, using Python and PySpark.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and cloud-based big data technologies like Apache Flink.
- Create and design Data Lake with AWS Services like S3 Lambda, Step Functions, Cloud Formation, Kinesis, and Firehose.
- Working knowledge of Apache Superset and Airbyte.
- Work with stakeholders including the Executive, Product, Data, and Design teams to assist with data-related technical issues and support their data engineering needs using data visualization tools like Tableau, Superset, Indicative
- Create data tools for analytics and data engineering team members that assist them in building and optimizing our product into an innovative industry leader, using machine learning frameworks like TensorFlow, PyTorch, or Scikit-Learn.
- Create segmentation using graph databases and knowledge with tools like Neo4j, AWS Neptune, or GraphQL.
- Document and communicate data engineering solutions and methodologies using tools like Confluence, and Jira.
- Assess and implement new data engineering tools and technologies to stay ahead in the industry.
- Troubleshoot and solve issues in the data engineering infrastructure using monitoring and debugging tools like ELK Stack.
Requirements :
- The candidate should have at least 3-5 years of experience in building AWS Data Platform Solutions or relative BI Experience.
- Experience in building and optimizing data pipelines using AWS/DBT ETL tools.
- Experience with Superset, Airbyte, AWS S3 Lambda, Step Functions, AWS Redshift.
- Experience in designing and deploying data platforms for supporting various data use cases including batch analysis, and real-time analytics.
- Experience using Python, PySpark, Kinesis, Firehose, and Kafka for real-time streaming analytical solutions.
- Experience in Data governance frameworks.
- Experience in MySQL and Python.
- Experience in Optimization of queries.
- Good understanding of Data warehousing concepts.
- Experience in Tableau, Hadoop, and AI/ML Data modeling.
- Experience in Hive, Hbase, and EMR clusters.
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