Top Data & Analytics Jobs

Lead the architecture and engineering standards for scalable data platforms and pipelines. Provide technical direction, conduct design reviews, and oversee Databricks administration, CI/CD, DataOps, observability, deployment, migration, and upgrades. Resolve complex platform challenges, mentor engineers, strengthen code quality, and partner with stakeholders on roadmaps and technical trade-offs. The role requires deep expertise in Databricks, Spark, Python, PySpark, dbt, and engineering governance.

Lead the design, development, deployment, and support of scalable data pipelines and platforms across on-premises and cloud environments. Build batch and near-real-time processing frameworks, modernize ETL and data warehouse workloads, implement governance and observability capabilities, troubleshoot production issues, and drive lakehouse adoption. Mentor engineers, collaborate with architects and stakeholders, evaluate emerging technologies, and ensure adherence to security, quality, and operational standards.

Leads the design, development, deployment, and support of scalable data pipelines and platforms across Hadoop, on-premises, and AWS cloud environments. Builds batch and near-real-time processing frameworks, modernizes ETL and data warehouse workloads, implements governance and observability capabilities, and troubleshoots production issues. Mentors engineers, collaborates with cross-functional stakeholders, evaluates emerging technologies, and ensures secure, high-quality delivery for enterprise-scale data ecosystems.

Build and maintain scalable batch, streaming, and real-time data pipelines, platforms, data lakes, warehouses, and lakehouses. Implement data quality, governance, security, observability, automation, and cloud modernization. Partner with product, engineering, analytics, data science, and business teams to deliver analytics, AI/ML, reporting, and customer insight solutions. Troubleshoot production issues, guide architecture, mentor engineers, and provide technical leadership across marketing data initiatives.

Build and maintain petabyte-scale data pipelines, entity-centric data models, and distributed workflows using Databricks and Spark. Partner with data science teams to productionize ML features and artifacts, optimize ETL/ELT processes, and implement testing, monitoring, observability, and incident response. Collaborate with governance and operations teams to ensure secure, compliant data management while supporting scalable merchant identity and trust-profile systems.

Develop and maintain scalable data pipelines, integrations, data lake structures, and analytical solutions for operational risk intelligence. Analyze incidents and risk events, identify trends and control gaps, and produce dashboards, reporting, and executive-ready insights. Troubleshoot data and workflow issues, improve automation and data quality, support governance and security standards, and collaborate with engineering teams and stakeholders on operational intelligence improvements.

Develops data-driven business strategies by translating analytics, market intelligence, and data science outputs into actionable recommendations. Leads market research, competitive intelligence, KPI frameworks, executive dashboards, reporting improvements, and AI-enabled analytics initiatives. Partners with Product, Data Science, Finance, and business leaders to influence product roadmaps, investments, growth priorities, and executive decisions through compelling data storytelling and strategic insights.

Lead the design and development of scalable cloud-native data platforms on AWS and Databricks. Build batch, streaming, and real-time pipelines using Spark and modern lakehouse technologies, migrate Hadoop workloads, improve platform reliability and performance, and support analytics, machine learning, and AI initiatives. The role also drives infrastructure automation, governance, security, observability, cost optimization, and collaboration across architecture, product, data science, and engineering teams.

Lead the architecture and development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and AI lifecycle capabilities. Drive security, governance, observability, reliability, performance, and cost optimization while translating R&D concepts into production systems. Provide hands-on technical leadership, architecture reviews, mentorship, and cross-functional collaboration.

Lead the architecture and hands-on development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and ML lifecycle capabilities. Establish standards for security, governance, testing, CI/CD, observability, reliability, and cost optimization while providing technical leadership, mentoring, architecture reviews, and cross-functional collaboration.