PwCPosted 11mo ago

ETIC, AWS Data Engineer, Senior Associate

ETIC, AWS Data Engineer, Senior Associate at PwC scores 64 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.

Cairo - ETICseniorFull time

AI in this role

databricks

Line of Service

Advisory

Industry/Sector

Technology

Specialism

Advisory - Other

Management Level

Senior Associate

Job Description & Summary

As a Senior Associate in the Data Engineering team, you will play a key role in designing, building, and optimizing modern data platforms and pipelines on AWS. You will work within cross-functional teams to deliver scalable, secure, and high-performing data solutions that enable advanced analytics, AI, and business insights for enterprise clients.
This role requires a strong understanding of cloud data architecture and hands-on experience with AWS data services for batch and real-time data engineering.

Key Responsibilities

Design, develop, and maintain end-to-end data pipelines across structured, semi-structured, and unstructured data sources.
Implement data ingestion, transformation, and orchestration frameworks leveraging AWS Glue, Amazon Redshift, AWS Step Functions, and Amazon Managed Workflows for Apache Airflow (MWAA).
Develop and optimize ETL/ELT processes using PySpark, SQL, and Delta Lake to ensure high performance and scalability.
Implement and enforce data quality, lineage, and governance practices.
Work closely with solution architects to design modern data architectures and ensure compliance with security and privacy standards.
Participate in client workshops and technical discussions to translate business needs into technical designs.

Required Skills & Experience

3–6 years of experience in data engineering, preferably in a consulting or enterprise environment.
Strong hands-on experience with:
- AWS Data Platform: AWS Glue, Amazon Redshift, Amazon S3, AWS Lambda, Amazon Kinesis, and AWS Step Functions.
Proficiency in Python and SQL for large-scale data processing and transformation.
Solid understanding of data modeling, medallion architecture, and lakehouse principles.
Familiarity with CI/CD pipelines, DevOps, and version control (e.g., Git, AWS CodePipeline).
Knowledge of data governance, lineage, and observability tools.
Experience with performance optimization, cost control, and best practices in cloud environments.

Education (if blank, degree and/or field of study not specified)

Degrees/Field of Study required:

Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, Communication, Creativity, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling, Data Pipeline {+ 27 more}

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Not Specified

Available for Work Visa Sponsorship?

Government Clearance Required?

Job Posting End Date

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Databricks

Questions you could be asked

What's a project where you used Databricks hands-on?
Describe a typical day in a role like this one: which parts run through AI directly?
If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

List these exact terms on your resume: Databricks. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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