Data Engineer- Associate-Analytics as service - Operate

At PwC, our people in managed services focus on a variety of outsourced solutions and support clients across numerous functions. These individuals help organisations streamline their operations, reduce costs, and improve efficiency by managing key processes and functions on their behalf. They are skilled in project management, technology, and process optimization to deliver high-quality services to clients. Those in managed service management and strategy at PwC will focus on transitioning and running services, along with managing delivery teams, programmes, commercials, performance and delivery risk. Your work will involve the process of continuous improvement and optimising of the managed services process, tools and services.

Driven by curiosity, you are a reliable, contributing member of a team. In our fast-paced environment, you are expected to adapt to working with a variety of clients and team members, each presenting varying challenges and scope. Every experience is an opportunity to learn and grow. You are expected to take ownership and consistently deliver quality work that drives value for our clients and success as a team. As you navigate through the Firm, you build a brand for yourself, opening doors to more opportunities.

Skills

Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:

• Apply a learning mindset and take ownership for your own development.
• Appreciate diverse perspectives, needs, and feelings of others.
• Adopt habits to sustain high performance and develop your potential.
• Actively listen, ask questions to check understanding, and clearly express ideas.
• Seek, reflect, act on, and give feedback.
• Gather information from a range of sources to analyse facts and discern patterns.
• Commit to understanding how the business works and building commercial awareness.
• Learn and apply professional and technical standards (e.g. refer to specific PwC tax and audit guidance), uphold the Firm's code of conduct and independence requirements.
  
  

Data Engineer – Experienced Associate (3–5 Years)

Our Analytics & Insights Managed Services team brings a unique combination of industry expertise, technology, data management and managed‐services experience to create sustained outcomes for our clients and improve business performance. We empower companies to transform their approach to analytics and insights while building your skills in exciting new directions. Have a voice at our table to help design, build and operate the next generation of software and services as a Data Engineer.

Basic Qualifications

Job Requirements and Preferences

• Minimum Degree Required: Bachelor's degree in computer science, Data Engineering, Information Systems, or a related technical field
• Minimum Years of Experience: 3–5 years of hands-on experience designing, building, and operating data solutions
  
  

Preferred Qualifications

• Degree Preferred: Master's degree in data science, Analytics, Computer Science, Information Systems, or related discipline
• Preferred Fields of Study: Data Processing/Analytics/Science, Management Information Systems, Software Engineering
  
  

Preferred Knowledge & Skills

As an Experienced Associate, you’ll work across the full data lifecycle—ingest, process, store, and serve—while collaborating with analytics, DevOps, and business teams:

• Data Pipeline Development – Design and implement ETL/ELT workflows using Python (PySpark), SQL, and shell scripting – Orchestrate batch and streaming jobs with Airflow, Prefect, or similar schedulers
• Big Data & Streaming Technologies – Hands-on with Apache Spark, Hadoop ecosystems (HDFS/YARN), and message queues (Kafka, Kinesis) – Integrate real-time and micro-batch data streams into downstream analytics platforms
  
  

– Build, Integrate and automate reporting dashboards using PowerBI, Tableau, Alteryx

• Cloud & Infrastructure – Deploy and manage data platforms on OCI/Azure/GCP (e.g. ADLS, Dataiku,Databricks, Snowflake) – Use Infrastructure-as-Code (Terraform, CloudFormation) and containerization (Docker, Kubernetes)
• Data Modeling & Warehousing – Define dimensional and normalized schemas for data marts, lakes, and warehouses (Redshift, Snowflake, BigQuery) – Apply best practices for partitioning, clustering, and indexing to optimize query performance
• DevOps & Automation – Build CI/CD pipelines for data code and infrastructure using Git, Jenkins/GitHub Actions, and testing frameworks – Automate monitoring, alerting, and rollback processes to ensure data reliability
• Data Governance & Quality – Implement data lineage, metadata management, and quality checks (Great Expectations, Deequ) – Partner with data stewards to enforce access controls, compliance, and cataloging
• Performance Tuning & Scalability – Profile and optimize Spark jobs, SQL queries, and storage layouts for high-volume workloads – Leverage caching, parallelism, and resource tuning to meet SLAs
• Collaboration & Communication – Work closely with data scientists, analysts, and architects to translate requirements into robust solutions – Present technical designs, status updates, and post-mortems to both technical and non-technical stakeholders