Data Engineering & Analytics Infrastructure Topics
Data pipeline design, ETL/ELT processes, streaming architectures, data warehousing infrastructure, analytics platform design, and real-time data processing. Covers event-driven systems, batch and streaming trade-offs, data quality and governance at scale, schema design for analytics, and infrastructure for big data processing. Distinct from Data Science & Analytics (which focuses on statistical analysis and insights) and from Cloud & Infrastructure (platform-focused rather than data-flow focused).
Data Quality and Validation
Covers the core concepts and hands on techniques for detecting, diagnosing, and preventing data quality problems. Topics include common data issues such as missing values, duplicates, outliers, incorrect labels, inconsistent formats, schema mismatches, referential integrity violations, and distribution or temporal drift. Candidates should be able to design and implement validation checks and data profiling queries, including schema validation, column level constraints, aggregate checks, distinct counts, null and outlier detection, and business logic tests. This topic also covers the mindset of data validation and exploration: how to approach unfamiliar datasets, validate calculations against sources, document quality rules, decide remediation strategies such as imputation quarantine or alerting, and communicate data limitations to stakeholders.
Compensation Data Modeling
Designing and using data models and queries to support compensation analysis and reporting. Topics include typical schema elements such as employee records with salary level tenure and location survey tables with market rates by job and percentile adjustments tables and decision history mapping rules between internal job codes and survey job families join logic aggregation strategies and preparing data for statistical testing. Candidates should describe how they implement comparisons and aggregations using structured query language or spreadsheet tools and how they ensure data quality traceability and auditability.
Compensation Data Systems and Automation
This topic covers practical skills for managing compensation data and automating compensation processes. Candidates should demonstrate the ability to extract and transform data from human resources information systems and payroll systems using Structured Query Language, automate repeatable analyses and data preparation tasks using Python or similar scripting languages, build reproducible data pipelines with validation checks, integrate external market data, and design efficient workflows for annual compensation reviews, benchmarking, and reporting. Candidates should also be able to identify process inefficiencies and implement technical solutions that reduce manual effort, improve accuracy, and preserve data privacy and auditability.
Salesforce Integration for Compensation Data
Assess experience and approach to integrating customer relationship management data with compensation systems for commission and incentive validation. Topics include extracting quota and attainment data, opportunity and booking records, and commission calculations from Salesforce; choosing integration approaches such as API exports, direct queries, or extract transform load pipelines; data modeling and join strategies to map sales events to payouts; reconciliation and validation checks; handling common data quality problems such as duplicates, missing currency or territory mappings, and timing mismatches; and building repeatable reports and audit trails to support payroll and commission processing.
Data Quality and System Integration Challenges
Focuses on data integrity, governance, and the operational issues that arise when data moves between systems. Candidates should be able to identify common data quality problems such as duplicates, missing or inconsistent fields, formatting mismatches, schema drift, and validation gaps. Understand how those issues propagate through integration pipelines and impact reporting, analytics, forecasting, and downstream processes. Discuss reconciliation strategies, validation rules, data cleansing, deduplication, master data management patterns, monitoring and alerting for data anomalies, and policies for schema evolution and versioning. Also cover practical approaches to prevent and remediate integration induced data errors and how to prioritize data quality work in revenue operations or cross system workflows.
Data Quality and Governance
Covers the principles, frameworks, practices, and tooling used to ensure data is accurate, complete, timely, and trustworthy across systems and pipelines. Key areas include data quality checks and monitoring such as nullness and type checks, freshness and timeliness validation, referential integrity, deduplication, outlier detection, reconciliation, and automated alerting. Includes design of service level agreements for data freshness and accuracy, data lineage and impact analysis, metadata and catalog management, data classification, access controls, and compliance policies. Encompasses operational reliability of data systems including failure handling, recovery time objectives, backup and disaster recovery strategies, observability and incident response for data anomalies. Also covers domain and system specific considerations such as customer relationship management and sales systems: common causes of data problems, prevention strategies like input validation rules, canonicalization, deduplication and training, and business impact on forecasting and operations. Candidates may be evaluated on designing end to end data quality programs, selecting metrics and tooling, defining roles and stewardship, and implementing automated pipelines and governance controls.