Sr Data Engineer

About the Role

Key Responsibilities

• Be a strong contributor in implementing data integration architecture and solutions.
• Conduct source system/data analysis and data profiling.
• Build robust data pipelines to collect, process and compute different metrics from various financial sources, adhering to quality and development standards.
• Design application architecture / technical design and articulate the data pipeline solutions to team members.
• Data mapping from source to target and define transformation rules that meet the models created by data Architects.
• Collaborate with cross functional team members as necessary and come up with optimal solutions that meet data demands.
• Execute unit test of data populated in target data container, validate expected result and ensure quality & accuracy.
• Coordinate with business users for user acceptance test and with operations team for code deployment to upper environments.
• Follow change management team stipulations on path to production requirements and strictly adhere to the compliance and regulatory needs.

Requirements

• Bachelor's Degree in Analytics, Mathematics, Statistics or Computer Science.
• Hands on experience in building data solutions in big data / cloud platforms.
• Experience with Hadoop ecosystem (HDFS, Hive, PIG, Hbase, Hiveql etc.).
• Experienced in ETL/ELT tools such as Talend, Datastage, Informatica, and Sqoop as well as building/enhancing ETL framework.
• RDBMS experience in either of Teradata, Oracle, MS-SQL, DB2, RedShift, Snowflakes, etc.
• Scripting language (shell scripting, SQL, PL/SQL, Python, Java, Spark, Scala).
• Experience with AWS cloud native services such as S3, RDS, Glue, Athena, etc.
• Worked with any of the scheduling tools (Autosys, Tidal, Oozie).
• SCM tool such as Bitbucket with Jira, Starteam or SVN.
• Experience working with various operating systems (Windows, Unix/Linux, IBM Mainframe).
• Strong communicator and collaborator with attention to details.
• Performance tuning and optimization of code and peer code reviews.
• 5+ years of professional work experience as described above.

Nice to Have

• BigData Technology/AWS/Agile Certifications.
• Data engineering experience for Data science projects where Machine learning is used (R, SAS, etc.).
• Experience in the financial services industry, especially banking.
• Business Intelligence/Reporting development experience using tools such as Tableau, Cognos, SAS, etc.
• Code commits to any Open source data tools.
• Experience with handling structured, semi-structured and unstructured data.

Qualifications

• Bachelor's Degree in Analytics, Mathematics, Statistics or Computer Science.
• 5+ years of professional work experience in data engineering roles.

Benefits & Perks

• A supportive and collaborative workforce.
• Access to training and tools to accelerate your potential and maximize your career growth.