You will analyze categorical features to determine the optimal encoding strategy based on cardinality and model fit considerations.

You will evaluate data quality metrics and document data transformation lineage to ensure transparency and reliability.

You will apply techniques to impute, flag, and validate missing or null values to produce consistent, model-ready datasets.

You will apply ETL and ELT pipelines to ingest data from various sources into a feature store using structured transformation workflows.

You will analyze upstream schema changes and implement safeguards to maintain data pipeline resilience and downstream compatibility.

You will evaluate data freshness, lag, and pipeline success rates against service level agreements to assess operational reliability.

You will apply version control branching strategies to manage code, experiments, and project artifacts effectively.

You will apply virtual environment tools to configure reproducible project environments with stable dependencies.

You will analyze resource utilization across CPU, GPU, and memory usage to optimize compute costs during experimentation.

In this project, you will design and implement a production-style machine learning data pipeline for a financial services risk modeling scenario. The raw dataset contains missing values, inconsistent categorical entries, potential outliers, and simulated schema drift. Your task is to transform this dataset into a validated, model-ready feature store. You will clean and preprocess structured tabular data, select encoding strategies based on feature cardinality, implement data validation using Great Expectations, detect schema changes between pipeline runs, generate SLA metrics to assess reliability, and save processed features in parquet format. Beyond the core pipeline, you will also apply professional development practices that are standard in production ML teams: setting up a virtual environment for reproducibility, using version control branching strategies to manage your work, and analyzing resource utilization to understand compute costs. Your final deliverable is a modular Python script and a structured written engineering explanation that demonstrates your ability to design reliable, production-aligned ML data infrastructure.