What You’ll Do
Apply the knowledge of data characteristics and data supply pattern, develop rules and tracking process to support data quality model.
Prepare data for analytical use by building data pipelines to gather data from multiple sources and systems.
Integrate, consolidate, cleanse and structure data for use by our clients in our solutions.
Perform design, creation, and interpretation of large and highly complex datasets.
Stay up-to-date with the latest trends and advancements in GCP and related technologies, actively proposing and evaluating new solutions.
Understand best practices for data management, maintenance, reporting and security and use that knowledge to implement improvements in our solutions.
Implement security best practices in pipelines and infrastructure.
Develop and implement data quality checks and troubleshoot data anomalies.
Provide guidance and mentorship to junior data engineers.
Review dataset implementations performed by junior data engineers.
What Experience You Need
BS degree in a STEM major or equivalent discipline; Master’s Degree strongly preferred
5+ years of experience as a data engineer or related role
Cloud certification strongly preferred
Advanced skills using programming languages such as Python or SQL and intermediate level experience with scripting languages
Intermediate level understanding and experience with Google Cloud Platforms and overall cloud computing concepts, as well as basic knowledge of other cloud environments
Experience building and maintaining moderately-complex data pipelines, troubleshooting issues, transforming and entering data into a data pipeline in order for the content to be digested and usable for future projects
Experience designing and implementing moderately complex data models and experience enabling optimization to improve performance
Demonstrates advanced Git usage and CI/CD integration skills
What Could Set You Apart
Master’s degree in a related field is a strong plus
Exposure in Vertex AI, GCP AI/ML services (AutoML, BigQuery ML, Cloud Run, etc.) or a similar cloud technology
Strong foundational skills in Linux Operating System.
Understanding of NLP, deep learning, and generative architectures (Transformers, Diffusion Models, etc.)
Background in credit risk, financial data analytics or risk modeling.
Experience working with large datasets on a big data platform (e.g., Google Cloud, AWS, Snowflake, Hadoop)
Experience in Business Intelligence, data visualization, and customer insights
generation.
Familiarity with data governance, model bias mitigation, and regulatory frameworks (GDPR, AI Act, SEC compliance).
Experience with MLOps practices, model monitoring, and CI/CD for AI workflows.
Knowledge of prompt tuning, fine-tuning, and parameter-efficient methods (LoRA, PEFT).
Hands-on experience with RAG, multi-modal AI, and hybrid AI architectures.
Contributions to the AI community through publications, open-source projects, or conference presentations.
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Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Engineering and Information Technology
Industries
Financial Services