What were the biggest challenges you faced in that project?

The biggest challenges I faced revolved around ensuring data quality and consistency from diverse, often legacy, upstream systems at scale, while also navigating complex cross-team dependencies and managing scope creep effectively.

Legacy data sources frequently presented issues like inconsistent schemas, unexpected null values, and data type mismatches, which could lead to pipeline failures or inaccurate analytics. At scale, these quality issues compounded, making processing inefficient and resource-intensive, especially when dealing with large datasets in systems like Spark or Snowflake. Cross-team dependencies required meticulous coordination of data contracts and schema changes to prevent breaking downstream consumers. Finally, managing scope creep was crucial to deliver features incrementally and avoid project delays.

A prime example was handling schema evolution from a critical operational database. Upstream changes, like a column's data type shifting from INT to STRING or new optional fields appearing, would frequently break our PySpark ingestion jobs. Our solution involved adopting a robust data lake architecture with Delta Lake. We landed raw data with schema inference, then applied a strict, versioned schema in our bronze layer. We used data quality checks (e.g., Great Expectations) and explicit casting with error handling during transformation. For instance, to ensure a critical ID column was always an integer, we'd use try_cast to gracefully handle malformed values, logging errors without crashing the pipeline, and defaulting to NULL or a placeholder if necessary. This allowed us to maintain data integrity while providing flexibility for upstream changes.

from pyspark.sql.functions import col, try_cast
# Example: Safely cast a potentially problematic ID column
df = df.withColumn("safe_id", try_cast(col("legacy_id"), "integer")) \
       .filter(col("safe_id").isNotNull()) # Filter out uncastable rows or handle them

In the interview, also mention the specific tools and frameworks you leveraged (e.g., Delta Lake, dbt, Great Expectations, Spark partitioning strategies) and quantify the impact of your solutions where possible.