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Mid-level generalist Data Engineer with broad cloud and ML skills attracts many applicants.
Core data engineering and cloud skills (BigQuery, Python, SQL) are highly transferable across industries.
Mandatory 4+ years plus specific GCP, BigQuery, Python, and ETL skills make shortlisting highly strict.
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Build and maintain an enterprise data lake on BigQuery including ingestion, transformation, and scheduling.
Design and implement ETL pipelines in Python integrating data from banking ERP/CRM sources such as NetSuite and Salesforce.
Develop and deploy machine learning models for forecasting and AI agents using large language model APIs (e.g., Claude, Gemini, OpenAI).
Minimum 4+ years of relevant work experience in data engineering or related roles.
Proficient in Python for ETL (Pandas, NumPy), SQL (complex queries, schema design, performance tuning), and Google Cloud Platform services including BigQuery, Cloud Storage, and CloudRun.
Experience with data lake design, ingestion from ERP/CRM systems, data quality monitoring, and REST APIs including OAuth and webhooks.
Familiarity with Git and CI/CD for version control and deployment.
Experienced working on large-scale data platforms integrating diverse enterprise data sources, particularly banking ERP/CRM systems.
Comfortable implementing and experimenting with machine learning forecasting models and integrating AI agents using advanced LLM APIs.
Skilled in cloud-native data engineering on GCP with practical knowledge of scheduling and orchestration tools like Airflow or Cloud Scheduler.