1 hour ago
Pune, IndiaStaff+
Responsibilities
- Design and implement enterprise-scale data hydration frameworks for ingestion, transformation, enrichment, validation, and distribution.
- Build scalable batch and real-time data pipelines across enterprise platforms.
- Prepare AI-ready enterprise data through cleansing, enrichment, chunking, tagging, and structuring.
- Implement data quality, lineage, reconciliation, metadata, observability, and governance controls.
- Integrate enterprise applications and data platforms using APIs, event streams, ETL/ELT patterns, and reusable services.
- Build resilient integration frameworks with monitoring, error handling, restartability, and operational controls.
- Optimize throughput, latency, reliability, and scalability across complex data processing workloads.
- Support Generative AI initiatives, including RAG and enterprise knowledge retrieval patterns.
- Collaborate with AI engineering, business, architecture, data, security, governance, and platform engineering teams.
- Support deployments, production issue resolution, root-cause analysis, and continuous improvement.
- Mentor junior engineers and promote engineering best practices.
Requirements
- Experience designing and implementing enterprise-scale data hydration frameworks.
- Experience building scalable batch and real-time data pipelines.
- Experience with data quality, lineage, reconciliation, metadata, observability, and governance controls.
- Experience integrating platforms using REST APIs, JSON/XML, event-driven integration, Kafka or streaming patterns, cloud integration concepts, and reusable interface design.
- Experience with Python, SQL, Git or GitLab CI/CD, Linux/Unix, and awareness of Azure, AWS, or GCP data platforms.
- Experience preparing governed data for AI consumption and familiarity with RAG, metadata, document processing, embeddings, and semantic search concepts.
- Experience collaborating on prompt engineering, LLM orchestration, AI evaluation, and responsible AI controls.
- Preferred experience with LLMs, RAG architecture, prompt engineering, agentic AI patterns, semantic search, vector databases, embedding models, AI evaluation, and AI observability.
- Preferred experience with Databricks, Apache Spark, Snowflake, Delta Lake, real-time streaming, Data Mesh concepts, enterprise data catalogues, and advanced metadata management.
- Preferred experience with AWS data services, Kafka, API management platforms, microservices, Event Hub, or Pub/Sub platforms.
- Preferred experience with GitLab CI/CD, Jenkins, automated testing frameworks, Infrastructure as Code, Docker/Kubernetes, production monitoring, and observability tools.
- Knowledge of banking or financial services, information security, privacy controls, GDPR, data retention, data entitlements, solution design, technical governance, stakeholder management, Agile delivery, mentoring, or consulting.
Benefits
- The role is based in Pune.
Tech Stack
Apache KafkaApache SparkAWSAzureDatabricksDockerGitGitLab CI/CDGoogle Cloud PlatformJenkinsKubernetesLinuxPythonSnowflakeSQL
Categories
Data Engineering
About Barclays
Our vision is to be the UK-centred leader in global finance. We are a diversified bank with comprehensive UK consumer, corporate and wealth and private banking franchises, a leading investment bank and a strong, specialist US consumer bank. Through these five divisions, we are working together for a better financial future for our customers, clients, and communities. For further information about Barclays, please visit home.barclays.