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Not ApplicableSpecialism
Data, Analytics & AIManagement Level
ManagerJob Description & Summary
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.Responsibilities:
1.Design and build robust streaming and batch data pipelines with AWS Glue, Lambda, Kinesis Data Streams/Firehose, Amazon MSK (Kafka), and Step Functions. 2.Architect end-to-end systems for real-time ingestion, processing, and analytics, leveraging Kinesis, MSK, Managed Flink, EMR, and Lambda for low-latency, high-availability operations. 3.Implement batch ETL pipelines using Glue, EMR (Spark/Hadoop), and S3, optimizing for cost and performance while handling large-scale datasets. 4.Integrate source systems (IoT, logs, transactional DBs, third-party feeds) and downstream destinations (Redshift, S3, DynamoDB, OpenSearch, Aurora, etc.). 5.Design and implement architectures supporting event-driven microservices, streaming analytics, materialized views, and near-real-time dashboards. 6.Ensure scalability, durability, ordering, and fault-tolerance in stream storage, using Kinesis/MSK and S3 for replay, backfill, and recovery scenarios. 7.Standardize patterns for Change Data Capture (CDC), late/duplicate data handling, data enrichment, stream normalization, and schema evolution. 8.Govern data security (encryption, access control, network isolation), ensure compliance, and embed monitoring/observability pipelines for SLA/SLO adherence. Required Experience and Skills 1.Experience in data engineering/architecture, including significant leadership in AWS cloud data platforms. 2.Expert in designing streaming architectures with Kinesis, Kafka (MSK), Managed Flink, Lambda, and Step Functions. 3.Strong proficiency in batch ETL (AWS Glue, EMR, Spark/Hadoop), large-scale data lake/lakehouse implementation (S3, Delta Lake, Redshift). 4.Hands-on experience architecting hybrid streaming/batch workflows for analytics, reporting, ML, and near-real-time applications. 5.Deep understanding of distributed computing, parallelization, and fault-tolerance strategies for high-velocity, high-volume data. 6.Familiarity with real-time dashboards, API publication (REST, WebSockets), and materialized views for user-facing analytics. Proficient in network/security controls, IAM design, encryption (KMS), and data governance. Experience with DevOps/IaC (CloudFormation, Terraform). 7.Excellent communication and stakeholder leadership; ability to translate business requirements to technical solution patterns.
Mandatory skill sets: AWS Data Architect
Preferred skill sets:
AWS Data Architect
Years of experience required:
8-12 Years
Education qualification: B. Tech/MBA/MCA / M. Tech
Education (if blank, degree and/or field of study not specified)
Degrees/Field of Study required: Bachelor of Technology, MBA (Master of Business Administration), Bachelor of Engineering Degrees/Field of Study preferred:Certifications (if blank, certifications not specified)
Required Skills
Data EngineeringOptional Skills
Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, Coaching and Feedback, 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 {+ 32 more}Desired Languages (If blank, desired languages not specified)
Travel Requirements
Available for Work Visa Sponsorship?
Government Clearance Required?
Job Posting End Date
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