ADP is hiring a Data Analytics Engineer

 

We're seeking a Data Analytics Engineer to build and maintain scalable data pipelines for payroll analytics and LLM feature engineering.

You'll work on event-driven data ingestion, transformation pipelines, and feature store development to support AI/ML initiatives and business analytics.

 

Key Responsibilities

Data Pipeline Engineering Design and implement event-driven data ingestion pipelines using SQS/SNS and Kafka Build Java-based integration services to consume and process events Develop and maintain Databricks-based ETL/ELT workflows

 

Feature Engineering for LLM & Analytics

Create feature datasets from ingested data to support LLM training and fine-tuning Design and implement feature stores using Python and SQL on Databricks Build data quality validation frameworks to ensure feature accuracy and consistency Develop data preprocessing pipelines for model training and inference

 

Data Platform Management

Maintain Databricks environments across multiple regions and deployment stages Optimize Spark jobs for performance and cost efficiency Implement data validation and monitoring solutions Manage canonical data models and transformation logic using SQL and PySpark

 

Analytics & Reporting

Build analytics-ready datasets for business intelligence and reporting Create data documentation and lineage tracking Support compliance and audit requirements through data validation pipelines

 

Must-Have Qualifications

Strong experience with Databricks platform and Apache Spark (PySpark) Proficiency in SQL for complex data transformations and analytics Hands-on experience with event-driven architectures using AWS SQS/SNS and/or Kafka Java development experience for building integration services and event consumers Python programming skills for data processing, feature engineering, and automation Experience building ETL/ELT pipelines for large-scale data processing Understanding of data modeling, schema design, and data warehouse concepts Familiarity with CI/CD practices and version control (Git) Strong problem-solving skills for data quality and pipeline optimization

 

Nice-to-Have

Experience preparing datasets for machine learning model training (especially LLMs) Knowledge of feature store architectures and MLOps practices Experience with multi-region data processing and compliance requirements Background in data governance and data quality frameworks Experience with Databricks Delta Lake and Unity Catalog Knowledge of AWS services Understanding of data security and privacy best practices Experience with test-driven development for data pipelines

 

Technical Environment

 

**Languages**: Python, SQL (Spark SQL), Java

**Platforms**: Databricks, AWS (SQS/SNS, S3)

**Frameworks**: Apache Spark, Kafka, PySpark

**Tools**: Git, pytest, Databricks CLI

 

What are you waiting for?  Apply today!

Find out why people come to ADP and why they stay: https://youtu.be/ODb8lxBrxrY

(ADA version: https://youtu.be/IQjUCA8SOoA )

 

*O modelo de trabalho adotado pela ADP é office based/presencial, com a possibilidade de trabalho em home-office por até duas vezes na semana.

*Considerando que as atividades desempenhadas pelos ocupantes deste cargo envolvem acesso a informações altamente confidenciais e sensíveis de clientes da ADP e de seus respectivos empregados, a ADP reserva-se o direito de conduzir checagem de histórico, de tempos em tempos, conforme autoriza o Incidente de Recurso de Revista Repetitivo nº 01 do Tribunal Superior do Trabalho, mediante consentimento do candidato/trabalhador.

*O modelo de trabalho adotado pela ADP é office based/presencial, com a possibilidade de trabalho em home-office por até duas vezes na semana.

*Considerando que as atividades desempenhadas pelos ocupantes deste cargo envolvem acesso a informações altamente confidenciais e sensíveis de clientes da ADP e de seus respectivos empregados, a ADP reserva-se o direito de conduzir checagem de histórico, de tempos em tempos, conforme autoriza o Incidente de Recurso de Revista Repetitivo nº 01 do Tribunal Superior do Trabalho, mediante consentimento do candidato/trabalhador.

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