Data Engineer – ADP (Automatic Data Processing)

Opportunity Overview

Data Pipeline & Validation Architecture

The Data Engineer at ADP focuses on consolidating complex, disparate HR and financial datasets into a cohesive data platform using PySpark and Databricks on AWS:

┌───────────────────────────────────────────┐
│ 1. Disparate Source Integration           │ ➔ Extract & integrate complex multi-source HR/Payroll datasets into AWS
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 2. PySpark & Databricks ETL Processing    │ ➔ Build high-performance batch pipelines using Spark SQL and PySpark
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 3. Automated Validation & Quality Checks  │ ➔ Implement strict automated data validation frameworks for accuracy & consistency
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 4. Downstream Analytics & Visualization   │ ➔ Expose clean datasets to enterprise analytics teams and Tableau dashboards
└───────────────────────────────────────────┘

Key Responsibilities

• ETL Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines supporting large-scale enterprise analytics and operational reporting.

• Disparate Data Integration: Unify data feeds from multiple disparate sources into a structured, central data platform.

• Data Validation & Integrity: Implement automated validation rules and auditing processes to ensure high data accuracy, schema consistency, and compliance.

• Query & Compute Optimization: Leverage PySpark, Spark core, and Databricks optimization techniques to accelerate data processing and query execution.

• Cross-Functional Collaboration: Partner with analytical teams and product stakeholders to deliver clean data solutions and optional Tableau dashboards.

Qualification Matrix & Technical Skill Stack

Core Requirements

Key Focus Areas for Interview Preparation

1. PySpark Memory Management & Performance Tuning: Practice explaining broadcast joins, repartitioning vs. coalescing, caching strategies, and handling data skewness in Spark jobs.

2. Data Validation Framework Design: Be prepared to detail how you build automated data assertion pipelines (e.g., row count checks, null-value checks, schema evolution handling).

3. AWS & Databricks Integration: Review best practices for executing Databricks workloads against AWS S3 data lakes and setting up cost-effective cluster configurations.