Document Data Conversion Mapping Rules

Context

In a fintech payments company processing 50M+ card transactions/day across multiple processors, you’re migrating from a legacy settlement system to a new warehouse model used for regulatory reporting (SOX), dispute operations, and revenue recognition. The migration requires dozens of field-level conversions: currency normalization, status code standardization, timestamp timezone rules, and PII handling. A single undocumented assumption (e.g., how to map a processor-specific decline code) can cause downstream metric drift, audit findings, or incorrect customer notifications.

Core question

How do you document conversion requirements and mapping rules for a SQL-based ETL/ELT pipeline so that engineers, analysts, and auditors can understand and validate the transformations?

Address the following:

Requirements capture: What information do you collect from stakeholders (finance, risk, ops) and from the source systems?
Source-to-target mapping spec: What columns/fields should a mapping document include (e.g., source field, target field, transformation logic, defaulting, null rules, data types, units, timezone, rounding, code mappings)?
Rule expression: How do you represent rules that are conditional (e.g., CASE WHEN) or depend on multiple fields/joins?
Edge cases & exceptions: How do you document unknown codes, late-arriving data, duplicate events, and conflicting sources of truth?
Validation & sign-off: What data quality checks, reconciliation queries, and acceptance criteria do you attach to each mapping?
Operationalization: How do you keep documentation in sync with SQL code (versioning, lineage, change management), and how do you make it discoverable?

Scope guidance (what the interviewer expects)

A practical, repeatable approach (templates, data contracts, mapping tables, dbt-style docs, or a metadata catalog)
Concrete examples of a mapping rule (e.g., status code normalization) and how it would be documented
Trade-offs between “docs in a wiki” vs “docs as data” (tables + tests + generated docs)
How your approach reduces ambiguity, supports audits, and prevents silent metric regressions

Context

Core question

How do you document conversion requirements and mapping rules for a SQL-based ETL/ELT pipeline so that engineers, analysts, and auditors can understand and validate the transformations?

Address the following:

Requirements capture: What information do you collect from stakeholders (finance, risk, ops) and from the source systems?
Source-to-target mapping spec: What columns/fields should a mapping document include (e.g., source field, target field, transformation logic, defaulting, null rules, data types, units, timezone, rounding, code mappings)?
Rule expression: How do you represent rules that are conditional (e.g., CASE WHEN) or depend on multiple fields/joins?
Edge cases & exceptions: How do you document unknown codes, late-arriving data, duplicate events, and conflicting sources of truth?
Validation & sign-off: What data quality checks, reconciliation queries, and acceptance criteria do you attach to each mapping?
Operationalization: How do you keep documentation in sync with SQL code (versioning, lineage, change management), and how do you make it discoverable?

Scope guidance (what the interviewer expects)

A practical, repeatable approach (templates, data contracts, mapping tables, dbt-style docs, or a metadata catalog)
Concrete examples of a mapping rule (e.g., status code normalization) and how it would be documented
Trade-offs between “docs in a wiki” vs “docs as data” (tables + tests + generated docs)
How your approach reduces ambiguity, supports audits, and prevents silent metric regressions

Context

Core question

How do you document conversion requirements and mapping rules for a SQL-based ETL/ELT pipeline so that engineers, analysts, and auditors can understand and validate the transformations?

Address the following:

Requirements capture: What information do you collect from stakeholders (finance, risk, ops) and from the source systems?
Source-to-target mapping spec: What columns/fields should a mapping document include (e.g., source field, target field, transformation logic, defaulting, null rules, data types, units, timezone, rounding, code mappings)?
Rule expression: How do you represent rules that are conditional (e.g., CASE WHEN) or depend on multiple fields/joins?
Edge cases & exceptions: How do you document unknown codes, late-arriving data, duplicate events, and conflicting sources of truth?
Validation & sign-off: What data quality checks, reconciliation queries, and acceptance criteria do you attach to each mapping?
Operationalization: How do you keep documentation in sync with SQL code (versioning, lineage, change management), and how do you make it discoverable?

Scope guidance (what the interviewer expects)

A practical, repeatable approach (templates, data contracts, mapping tables, dbt-style docs, or a metadata catalog)
Concrete examples of a mapping rule (e.g., status code normalization) and how it would be documented
Trade-offs between “docs in a wiki” vs “docs as data” (tables + tests + generated docs)
How your approach reduces ambiguity, supports audits, and prevents silent metric regressions

Context

Core question

How do you document conversion requirements and mapping rules for a SQL-based ETL/ELT pipeline so that engineers, analysts, and auditors can understand and validate the transformations?

Address the following:

Requirements capture: What information do you collect from stakeholders (finance, risk, ops) and from the source systems?
Source-to-target mapping spec: What columns/fields should a mapping document include (e.g., source field, target field, transformation logic, defaulting, null rules, data types, units, timezone, rounding, code mappings)?
Rule expression: How do you represent rules that are conditional (e.g., CASE WHEN) or depend on multiple fields/joins?
Edge cases & exceptions: How do you document unknown codes, late-arriving data, duplicate events, and conflicting sources of truth?
Validation & sign-off: What data quality checks, reconciliation queries, and acceptance criteria do you attach to each mapping?
Operationalization: How do you keep documentation in sync with SQL code (versioning, lineage, change management), and how do you make it discoverable?

Scope guidance (what the interviewer expects)

A practical, repeatable approach (templates, data contracts, mapping tables, dbt-style docs, or a metadata catalog)
Concrete examples of a mapping rule (e.g., status code normalization) and how it would be documented
Trade-offs between “docs in a wiki” vs “docs as data” (tables + tests + generated docs)
How your approach reduces ambiguity, supports audits, and prevents silent metric regressions

Interview Guides

Context

Core question

Scope guidance (what the interviewer expects)

Document Data Conversion Mapping Rules

Context

Core question

Scope guidance (what the interviewer expects)

Your Answer

Document Data Conversion Mapping Rules

Context

Core question

Scope guidance (what the interviewer expects)

Document Data Conversion Mapping Rules

Context

Core question

Scope guidance (what the interviewer expects)

Your Answer