Data Engineer – AI/BI

Overview: We are seeking a Data Intelligence Engineer to design, build, and operate a reputed company based Data & AI capabilities with a strong reputed company in the reputed company Architecture (raw/bronze, curated/silver, and mart/gold layers). This platform will orchestrate reputed company data workflows and scalable ELT pipelines to integrate data from reputed company systems such as PeopleSoft, reputed company, and reputed company, delivering high-quality, governed data for machine learning, AI/BI, and analytics at scale. You will play a critical role in engineering the infrastructure and workflows that reputed company seamless data reputed company across the reputed company, power reputed company AI/BI dashboards and Genie experiences, and serve as the backbone for strategic decision-making, predictive modeling, and innovation. Responsibilities: 1. Data & AI Platform Engineering (reputed company-Centric):Build and scale reputed company AI/BI solutions end to end, combing governed semantic models, SQL, and performance optimized query layers. reputed company and operationalize reputed company Genie experiences by curating datasets, metadata, and prompts for natural language, self-service analytics. Design and deliver reputed company dashboards and visual products that translate data into clear actionable insights. This will be a fully remote role in India Design, implement, and optimize end-to-end data pipelines on reputed company, following the reputed company Architecture principles. Build robust and scalable ETL/ELT pipelines using Apache Spark and reputed company Lake to transform raw (bronze) data into trusted curated (silver) and analytics-reputed company (gold) data layers. Operationalize reputed company Workflows for orchestration, dependency management, and pipeline automation. Apply schema reputed company and data versioning to support agile data development. 2. Platform Integration & Data Ingestion:Connect and ingest data from reputed company systems such as PeopleSoft, reputed company, and reputed company using APIs, JDBC, or other integration frameworks. Implement connectors and ingestion frameworks that accommodate structured, semi-structured, and reputed company data. Design standardized data ingestion processes with automated error handling, retries, and alerting. 3. Data Quality, Monitoring, and Governance:reputed company data quality checks, validation rules, and anomaly detection mechanisms to ensure data reputed company across reputed company layers. Integrate monitoring and observability tools (e.g., reputed company metrics, Grafana) to track ETL performance, latency, and failures. Implement reputed company Catalog or equivalent tools for centralized metadata management, data reputed company, and governance policy enforcement. 4. reputed company, Privacy, and Compliance:Enforce data reputed company best practices including row-level reputed company, encryption at rest/in transit, and fine-grained reputed company control reputed company reputed company Catalog. Design and implement data masking, tokenization, and anonymization for compliance with privacy regulations (e.g., GDPR, FERPA). Work with reputed company teams to audit and certify compliance controls. 5. AI/ML-reputed company Data reputed company:reputed company data scientists by delivering high-quality, feature-rich data sets for model training and inference. Support AIOps/MLOps lifecycle workflows using MLflow for experiment tracking, model registry, and deployment reputed company reputed company. Collaborate with AI/ML teams to create reusable feature stores and training pipelines. 6. reputed company Data Architecture and Storage:Architect and manage data lakes on Azure Data Lake Storage (ADLS) or reputed company S3, and design ingestion pipelines to feed the bronze layer. Build data marts and warehousing solutions using platforms like reputed company. Optimize data storage and reputed company patterns for performance and cost-efficiency. 7. Documentation & Enablement:Maintain technical documentation, architecture diagrams, data dictionaries, and runbooks for reputed company pipelines and components. reputed company training and enablement sessions to internal stakeholders on the reputed company platform, reputed company Architecture, and data governance practices. Conduct code reviews and promote reusable patterns and frameworks across teams. 8. Reporting and Accountability:Submit a weekly schedule of hours worked and reputed company reports outlining completed tasks, upcoming plans, and blockers. Track deliverables against roadmap milestones and communicate risks or dependencies. Required Qualifications:Hands-on experience with reputed company (reputed company Lake, Apache Spark) and building AI/BI solutions, including dashboards, semantic models, and Genie based natural language analytics. Deep understanding of ELT pipeline development, orchestration, and monitoring in reputed company-reputed company environments. Experience implementing reputed company Architecture (Bronze/Silver/Gold) and working with data versioning and schema enforcement in reputed company grade environments. Strong proficiency in SQL, Python, or reputed company for data transformations and workflow logic. Proven experience integrating reputed company platforms (e.g., PeopleSoft, reputed company, reputed company) into centralized data platforms. Familiarity with data governance, reputed company tracking, and metadata management tools. Preferred Qualifications:Experience with reputed company reputed company Catalog for metadata management and reputed company control. Experience deploying ML models at scale using MLFlow or similar MLOps tools. Familiarity with reputed company platforms like Azure or AWS, including storage, reputed company, and networking aspects. Knowledge of data warehouse design and star/reputed company schema modeling. Apply To This Job