Staff Data Engineer (São Paulo - Brazil)

NPS Prism; São Paulo; Sur place; BRL 223 200 - 334 800; Plein temps

Résumé du poste

A leading analytics company in São Paulo is seeking an experienced Staff Data Engineer to optimize data pipelines and architectures. The ideal candidate has deep expertise in Databricks, Python, SQL, and PySpark, along with significant experience in data engineering roles. Responsibilities include designing scalable data solutions, mentoring junior engineers, and ensuring data quality across platforms. This is a full-time, mid-senior level position.

Qualifications

• 6–8 years of data engineering experience, with at least 3 years in a lead or staff-level role.
• Proven ability to design end-to-end data solutions and influence engineering best practices.
• Strong mentorship and stakeholder management skills.

Responsabilités

• Design and own scalable data architectures for ingestion and analytics.
• Build robust ETL/ELT pipelines using PySpark and SQL.
• Mentor junior data engineers and enforce best practices for code quality.

Connaissances

Python

SQL

PySpark

ETL/ELT design

Delta Lake

DevOps

CI/CD pipelines

Kafka

Formation

Bachelor’s degree in Computer Science or related field

Outils

Databricks

Azure Data Lake

AWS S3

GCP BigQuery

Azure Data Factory

AWS Glue

Description du poste

Overview

We are seeking an experienced Staff Data Engineer to join the NPS Prism engineering team – a Bain platform that provides advanced analytics, benchmarking, and insights into customer experience metrics across industries.

As a senior technical leader, you will be responsible for designing, building, and optimizing large-scale, high-performance data pipelines and architectures that power NPS Prism’s analytics and client-facing applications. This role requires deep Databricks expertise, proficiency in Python, SQL, and PySpark, and the ability to work across cloud-native environments (Azure, AWS, or GCP).

You’ll collaborate closely with data scientists, product managers, and business stakeholders to shape and execute the platform’s data strategy, ensuring data quality, scalability, and reliability at enterprise scale.

Key Responsibilities

Data Architecture & Engineering Leadership

Design and own scalable data architectures for ingestion, transformation, and analytics on Databricks.

Build robust ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.

Lead performance tuning, partitioning, and data optimization across large distributed systems.

Mentor junior data engineers and enforce best practices for code quality, testing, and version control.

Develop and maintain data lakes and data warehouses on cloud platforms (Azure Data Lake, AWS S3, GCP BigQuery, etc.).

Utilize Azure Data Factory, AWS Glue, or similar orchestration tools to manage large-scale data workflows.

Integrate multiple data sources (structured, semi-structured, and unstructured) into unified models for NPS Prism analytics.

Databricks & Advanced Analytics Enablement

Leverage Databricks for large-scale data processing, Delta Lake management, and ML/AI enablement.

Drive the adoption of Databricks Unity Catalog, governance, and performance features.

Partner with analytics teams to enable seamless model training and inference pipelines on Databricks.

Data Quality, Observability & Governance

Define and implement frameworks for data validation, monitoring, and error handling.

Collaborate with platform teams to establish data lineage and governance using tools like Great Expectations, Monte Carlo, or Databricks-native observability.

Ensure compliance with Bain’s data security and privacy standards.

DevOps & CI/CD for Data

Automate testing, deployment, and monitoring for data workflows to ensure reliability and repeatability.

Cross-Functional Collaboration

Work with product and business teams to translate analytical requirements into scalable technical designs.

Collaborate with Data Science and BI teams to deliver analytics-ready datasets for dashboards and models.

Serve as a technical advisor in architectural reviews and strategic data initiatives within NPS Prism.

Required Skills and Qualifications

Core Technical Expertise:

Strong command of Python, SQL, and PySpark for big data processing.

Experience with Delta Lake, Spark optimization, and cluster management.

Hands‑on with ETL/ELT design, data lake and warehouse architecture.

6–8 years of data engineering experience, with at least 3 years in a lead or staff‑level role.

Proven ability to design end‑to‑end data solutions and influence engineering best practices.

Strong mentorship and stakeholder management skills.

Familiarity with streaming frameworks (Kafka, Event Hubs).

Exposure to DevOps, CI/CD pipelines, and Infrastructure as Code (IaC).

Strong problem‑solving and analytical skills.

Educational Qualifications

Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field.

Seniority level

Mid‑Senior level

Employment type

Full‑time

Job function

Sales, General Business, and Education. Industries: Wireless Services, Telecommunications, and Communications Equipment Manufacturing.

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