• US Citizen

• Green Card

• EAD (OPT/CPT/GC/H4)

• H1B Work Permit

• Corp-Corp

• Contract to Hire

• Consulting/Contract

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 26th Nov 2022

Key skills : Strong SQL/Data warehousing concepts/PySpark/AWS 

KEY RESPONSIBILITIES

• Design, implement and support an analytical data infrastructure
• Managing AWS resources including EC2, EMR, S3, Glue, Redshift, etc.
• Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies
• Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency
• Collaborate with Data Scientists and Business Intelligence Engineers (BIEs) to recognize and help adopt best practices in reporting and analysis
• Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers
• Maintain internal reporting platforms/tools including troubleshooting and development. Interact with internal users to establish and clarify requirements in order to develop report specifications.
• Work with Engineering partners to help shape and implement the development of BI infrastructure including Data Warehousing, reporting and analytics platforms.
• Contribute to the development of the BI tools, skills, culture and impact.
• Write advanced SQL queries and Python code to develop solutions
SKILL SETS :
• This position requires a Bachelor's Degree in Computer Science or a related technical field, and relevant employment experience
• Work experience with ETL, Data Modelling, and Data Architecture.
• Expert-level skills in writing and optimizing SQL.
• Experience with Big Data technologies such as Hadoop/Hive/Spark.
• Solid Linux skills.
• Experience operating very large data warehouses or data lakes.
• Expertise in ETL optimization, designing, coding, and tuning big data processes using Apache Spark or similar technologies.
• Experience with building data pipelines and applications to stream and process datasets at low latencies.
• Show efficiency in handling data - tracking data lineage, ensuring data quality, and improving discoverability of data.
• Sound knowledge of distributed systems and data architecture (lambda)- design and implement batch and stream data processing pipelines, knows how to optimize the distribution, partitioning, and MPP of high-level data structures.
• Knowledge of Engineering and Operational Excellence using standard methodologies.