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Common senior data engineering title with mid-level experience and broad skillset increases competition.
Core data engineering skills (ETL, cloud, Spark, SQL) are highly transferable across industries.
Explicit 5–8 years plus mandatory cloud, Spark, BigQuery, SQL, and Python requirements make filters stringent.
Design, deploy, and maintain organization's data architecture aligned with business goals.
Lead and mentor data engineering team on pipelines, ETL, and data integration solutions.
Manage data security, governance policies, and optimize large-scale data storage and retrieval systems.
5-8 years of relevant work experience in data engineering and architecture.
Bachelor's degree in Computer Science, IT, Data Science, Engineering, or related field.
Experience with Google Cloud, Spark, Machine Learning, BigQuery, SQL, and Python.
Not explicitly mentioned: notice period or strict location requirements.
Experienced in designing scalable data architectures supporting business objectives.
Familiarity with cloud platforms (Azure, AWS, GCP) and modern data engineering tools (Databricks, Snowflake).
Proven leadership in managing data engineering teams and cross-functional collaboration with IT stakeholders.