Top Data & Analytics Jobs

Build and optimize scalable batch and streaming data pipelines using Python, PySpark, SQL, and Apache Spark on AWS. Develop EMR data processing jobs, lead an Apache Airflow orchestration proof of concept, integrate Amazon Q for data quality and documentation, and implement GitLab CI/CD with infrastructure-as-code. The role emphasizes data governance, security, automation, performance, scalability, and cost efficiency while collaborating in Agile teams and presenting proof-of-concept findings.

Designs and leads enterprise data architecture modernization across cloud and lakehouse environments. Responsibilities include data modeling, Medallion architecture, storage tiering, vector databases, RAG, semantic layers, query and compute optimization, governance, access controls, lineage, integration, and compliance. The role partners with architecture, engineering, and business teams to deliver scalable, secure, highly available data assets and supports AI initiatives.

Design and optimize ETL processes, data ingestion systems, streaming applications, and cloud data architectures using SQL, Python, Snowflake, AWS, and Redshift. Maintain and troubleshoot production data pipelines, integrate analytical outputs with business intelligence applications, optimize queries, and collaborate with analysts and engineers using Airflow and Databricks. Evaluate AI tools and emerging technologies to improve data warehouse workflows.

Designs enterprise data architecture, governance processes, data models, integration frameworks, and automated pipelines supporting billing and credit systems. Establishes data quality, validation, reconciliation, metadata, and documentation standards; troubleshoots pipeline and system issues; evaluates data technologies; and collaborates with engineering, IT, and business stakeholders to deliver scalable, reliable data solutions.

Develop and optimize cloud-based data pipelines for analytics, machine learning, LLM feature stores, and AI applications. Build AI agents, integrate large language models and vector datasets, implement monitoring and self-healing workflows, and establish data observability for freshness, lineage, and performance. The role also structures semantic data layers, improves resource utilization and cloud costs, troubleshoots distributed platforms, and collaborates across teams to align data infrastructure with business needs.

Design, migrate, and optimize real-time data pipelines into Databricks and SQL Server, build high-performance SQL workflows, create Tableau dashboards, develop sales projection models, integrate generative AI for transcript/coaching automation, and enforce data cleansing and auditing for Direct Sales leadership.

Build and maintain automated, AI-powered billing and credit reports and forecasting models. Migrate legacy workflows into scalable pipelines, collaborate cross-functionally, develop generative AI prompts and models, and deliver executive-level insights to improve billing operations.