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Strong employer brand, mid-level generalist role, metro location, and broad skill requirements increase applicant competition.
Data engineering skills (SQL, Python, ETL) are easily transferable across industries.
Explicit 2–4 years plus required data engineering skills make screening moderately strict.
Build and maintain data pipelines, data transformations, and curated datasets to support analytics solutions.
Support feature engineering, MLOps workflows, model data preparation, and monitoring activities.
Contribute to CI/CD processes, data validation, quality checks, lineage tracking, and collaborate with engineers, analysts, and data scientists.
2–4 years of experience in data or analytics engineering.
Bachelor’s degree in computer science, engineering, or related discipline.
Working knowledge of SQL, Python, and data processing; exposure to AWS cloud services and ETL/ELT concepts.
Familiarity with data quality, validation practices, DevOps, CI/CD principles, and orchestration tools like Apache Airflow.
Experience working in analytics engineering or data engineering roles supporting machine learning and data science teams.
Comfortable applying AI tools for coding, testing, documentation, and automation to improve development productivity.
Interest in automation, continuous learning, and working in a collaborative environment with exposure to MLOps and streaming data concepts.