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Job Location | Delhi |
Education | Not Mentioned |
Salary | Not Disclosed |
Industry | Not Mentioned |
Functional Area | Not Mentioned |
EmploymentType | Full-time |
Data EngineerExperience- 4-8 YearsBudget- Upto 17 LakhsNotice Period- 30 Days or LessLocation- Delhi/Bangalore ( Should be ready to relocate as per requirement)Must have Skills- Python, HDFS, HiveResponsibilities and Duties: Identify areas of improvement in the current data pipeline and revamp the same to improve dataquality, add source and sink data connectors, improve stability and reduce data processing time Work closely with data scientists to develop and incorporate data models Develop the backend and front end of data quality dashboards which provides insights into theavailability and quality of incoming data Develop pre-processing pipelines to handle streaming data and output batched data into data lake Work closely with the product team to identify data requirements which can be streamlined intoscheduled streams and with DE lead to identify new relevant technologies Develop POC and perform experiments to take data backed decisions while evaluating newtechnologies Develop automated workflows which process data streams on trigger/scheduled intervalsSpecific Skills: In-depth knowledge of Big Data technologies - Spark, HDFS, Hive, Presto, Druid and with cloud envssuch as AWS, GCP, Azure Expert knowledge of Python programming Experience working with workflow managers like Airflow, Prefect, Luigi, Oozie Experience working with Data Governance tools like Apache Atlas, Ranger Experience working with streaming data with technologies like Kafka, Spark streaming Strong understanding of big data performance tuning Experience handling different kinds of structured and unstructured data formats (Parquet/DeltaLake/Avro/XML/JSON/YAML/CSV/Zip/Xlsx/Text etc.) Experience working with distributed NoSQL storage like ElasticSearch, Apache Solr and relational Dbincluding MySQL, Postgres, Redshift, Teradata Experience working with ETL systems like Informatica, Talend, SSIS Software Development Life Cycle Methodologies and Practices.