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Mid-level title, metro location, broad toolset, and common role increase applicant competition.
Modern data stack and cloud focus make skills transferable, but consulting experience and specific tooling raise fit sensitivity.
Explicit 2-4 years plus mandatory cloud, modern data stack, and AI-assistant proficiency increases shortlisting strictness.
Design, build, and maintain reliable batch and real-time data pipelines and ingestion code for clients.
Develop cloud-native ELT/ETL pipelines and Lakehouse solutions in production cloud environments with onshore and offshore teams.
Prepare clean, well-modeled data to support analytics, dashboards, AI/ML, and GenAI use cases including LLM-based applications.
2-4 years of hands-on data engineering experience.
Strong SQL and Python programming skills with production code delivered.
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and modern data stack tools such as Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, or Fivetran.
Mandatory ability to use AI coding assistants (e.g., GitHub Copilot, Codex) along with strong programming fundamentals.
Experienced in consulting roles with ability to deliver independently and interface directly with clients.
Skilled in supporting AI, ML, and GenAI use cases by preparing data for models, feature stores, and vector databases.
Familiar with data engineering best practices including version control (Git), CI/CD, and architectural standards within fast-paced projects.