Big Data lead

We at Onix Datametica Solutions Private Limited are looking for Bigdata Lead who have a passion for cloud with knowledge of different on-premise and cloud Data implementation in the field of Big Data and Analytic s including and not limiting to Teradata, Netezza, Exadata, Oracle, Cloudera, Hortonworks and alike. Ideal candidates should have technical experience in migrations and the ability to help customers get value from Datametica's tools and accelerators Job Description 6+ years of overall experience in developing, testing implementing Big data projects using Hadoop, Spark, Hive Hands-on experience playing lead role in Big data projects, responsible for implementing one or more tracks within projects, identifying and assigning tasks within the team and providing technical guidance to team members Experience in setting up Hadoop services, implementing ETL/ELT pipelines, working with Terabytes of data ingestion processing from varied systems Experience working in onshore/offshore model, leading technical discussions with customers, mentoring and guiding teams on technology, preparing HDD LDD documents Required Skills and Abilities: Mandatory Skills Spark, Scala/Pyspark, Hadoop ecosystem including Hive, Sqoop, Impala, Oozie, Hue, Java, Python, SQL, Flume, bash (shell scripting) Experience implementing CICD pipelines and working experience with tools like SCM tools such as GIT, Bit bucket, etc Hands on experience in writing data ingestion pipelines, data processing pipelines using spark and SQL, experience in implementing SCD type 1 2, auditing, exception handling mechanism Data Warehousing projects implementation with either, Scala or Hadoop programming background Proficient with various development methodologies like waterfall, agile/scrum Exceptional communication, organisation, and time management skills Collaborative approach to decision-making Strong analytical skills Good To Have - Certifications in any of GCP, AWS or Azure, Cloud era Work on multiple Projects simultaneously, prioritising appropriately