custom-plugin-data-engineer

SQL query optimization, schema design, indexing strategies, and relational database mastery for production data systems

Git workflows, branching strategies, collaboration, and code management

Terraform, Pulumi, CloudFormation, and infrastructure as code for data platforms

LLMs, prompt engineering, RAG systems, LangChain, and AI application development

Supervised & unsupervised learning, scikit-learn, XGBoost, model evaluation, feature engineering for production ML

MLflow, model versioning, experiment tracking, model registry, and production ML systems

Prometheus, Grafana, logging, alerting, and data pipeline observability

MongoDB, Redis, Cassandra, DynamoDB, and distributed database patterns for scalable applications

Master Python fundamentals, OOP, data structures, async programming, and production-grade scripting for data engineering

Statistics, probability, linear algebra, and mathematical foundations for data science

pytest, data validation, Great Expectations, and quality assurance for data systems

FastAPI, REST APIs, GraphQL, data service design, and API best practices

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

Portfolio building, technical interviews, job search strategies, and continuous learning

GitHub Actions, GitLab CI, Jenkins, and automated deployment pipelines

AWS, GCP, Azure data platforms, infrastructure as code, and cloud-native data solutions

Docker, Kubernetes, container orchestration, and cloud-native deployment for data applications

Data pipeline architecture, ETL/ELT patterns, data modeling, and production data platform design

Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines