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Metro-based, mid-level Data Engineer role with generalist skills creates high applicant competition.
Core data engineering skills transferable, but required finance and manufacturing domain expertise raises sensitivity.
Multiple mandatory tech stacks, cloud DWH and explicit years increase filter strictness.
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Lead design and development of data warehousing solutions including Snowflake and optimize ETL/ELT pipelines using AWS Glue.
Provide technical guidance and mentorship to the data engineering team, ensuring best practices in data integration and data quality.
Apply AI/ML tools including Cortex AI and AWS Bedrock to extract insights and integrate AI solutions within data warehousing environments, particularly for Finance, Accounts, and Manufacturing domains.
3 to 5 years overall experience with at least 2 years in Data Analytics and Digital solutions involving Lake house architecture.
BTech degree in Computer Science or IT.
Proficiency in data models, data mining, SQL database design, and programming languages like Java and Python.
Experience with Cloud ETL/ELT, Cloud Data Warehousing (Snowflake), and exposure to AI/ML tools; domain knowledge in Finance, Accounts, and Manufacturing.
Experienced in managing and optimizing data warehousing architecture on cloud platforms with a focus on automation and AI integration.
Strong domain expertise in Finance, Accounts, and Manufacturing to translate business requirements into technical solutions.
Capable of technical leadership, mentorship, and stakeholder collaboration within agile software development environments.