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Mid-level popular Data Engineer role with metro location and broad toolset requirements increases competition.
Core data engineering skills are highly transferable across industries despite optional domain-specific tool preferences.
Explicit 4–10 years plus mandatory cloud, SQL, Python/PySpark, and orchestration tooling requirements.
Design, develop, and maintain scalable ETL/ELT data pipelines and data infrastructure supporting analytics, reporting, and AI/ML initiatives.
Build and maintain data models, warehouses, and semantic layers on cloud platforms (AWS, GCP, Azure) using tools like Airflow, dbt, or Data Factory.
Collaborate with Data Architects, Scientists, and business stakeholders to translate requirements into technical solutions and ensure data quality and governance.
4 to 10 years of relevant Data Engineering or related experience.
Proficiency in advanced SQL, Python and/or PySpark, and experience with cloud data platforms and associated data warehouse/lake services (Snowflake, BigQuery, Databricks, Microsoft Fabric).
Hands-on experience with workflow orchestration tools such as Airflow, dbt, Cloud Composer, or Data Factory.
Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.
Experienced in end-to-end ownership of cloud-native data pipelines and strong problem-solving in data engineering context.
Able to collaborate effectively with cross-functional teams including technical and non-technical stakeholders.
Prior exposure or interest in advanced data engineering domains such as MLOps, unstructured data/GenAI, or BI enablement is a plus but not mandatory.