Jobs at Tiger Analytics

Designs, builds, and deploys production-grade data engineering, analytics, GenAI, and agentic AI solutions for enterprise clients on Databricks. Responsibilities include Lakehouse architecture, data pipeline development, AI/ML workflows, RAG and natural-language analytics solutions, cloud migration, legacy BI modernization, performance optimization, technical workshops, consultative discovery, and cross-functional leadership with clients and Databricks teams.

Leads complex, cross-functional technology programs from planning through execution and transition. Responsibilities include developing integrated roadmaps, governance, financial and resource plans, executive reporting, dependency and risk management, stakeholder communication, and decision closure. The role requires technical fluency across data, analytics, platforms, security, and operations, plus experience with data risk, privacy, non-production data protection, regulatory remediation, and regulated financial-services environments.

Drive AWS-influenced pipeline, bookings, and revenue growth across Financial Services and Insurance. Build relationships with AWS field sellers, identify enterprise transformation opportunities, develop joint account strategies, shape complex deals, and own pursuits through closure. The role requires deep AWS partner ecosystem knowledge, financial services industry relationships, executive engagement, pipeline management, forecasting, and experience selling data, analytics, AI, generative AI, and cloud transformation services.

Designs enterprise AI and data architectures across SAP landscapes using BTP and BDC. Responsibilities include defining AI roadmaps, governing data products, architecting generative AI, RAG, copilot, and agent solutions, integrating cloud and third-party models, and applying clean-core extensibility. The role also provides technical leadership across implementation, deployment, monitoring, and cost control while engaging clients, SAP, system integrators, and hyperscalers.

Design and govern enterprise semantic models using AtScale, SML, and Snowflake. Translate business requirements into standardized metrics, KPIs, dimensions, hierarchies, and business definitions. Optimize data models, SQL workloads, and aggregates; implement Snowflake security controls; integrate models with enterprise BI platforms; and support AI/ML use cases. Establish Git-based CI/CD, testing, deployment, lineage, governance, auditability, and regulatory compliance for banking and financial services data.

Designs and implements advanced analytics capabilities by building Azure-based data platforms, ingestion pipelines, and scalable data processing solutions. The role uses Python, Spark SQL, Azure Data Factory, Azure Storage, and Azure Databricks to transform structured and unstructured data. Responsibilities also include querying large datasets, applying Azure networking and security practices, and potentially using Snowflake to create data marts.

Lead end-to-end delivery of enterprise Generative AI and Agentic AI programs, coordinating data science, machine learning, data engineering, product, architecture, and client teams. Oversee RAG pipelines, multi-agent ecosystems, knowledge graphs, production deployment, risks, dependencies, and technical challenges. Communicate program progress, blockers, and decisions to senior leadership and stakeholders while ensuring successful delivery of complex AI initiatives.

Lead complex data engineering engagements, manage stakeholders and risks, coordinate offshore teams, and build scalable Azure data pipelines. Develop ingestion and transformation solutions using Azure Databricks, Data Factory, Spark, Delta Lake, SQL, and Python. Harmonize raw data into consumer-ready formats, query large datasets, support on-premises cloud migration, and collaborate with executives and delivery teams.

Leads growth for the Insurance business through hyperscaler and data-platform partnerships. Builds partner relationships, identifies client opportunities, develops AI/ML proposals, expands accounts, and drives market growth. Translates client needs into enterprise data science and AI solutions, leads solution deployment, defines data science roadmaps, mentors technical teams, and strengthens AI/ML service offerings. Requires extensive Data and AI consulting, enterprise solution delivery, business development, and partner ecosystem experience.

Designs and prototypes scalable agentic AI architectures across Azure, AWS, or GCP. Responsibilities include multi-agent orchestration, LLM applications, RAG pipelines, memory and vector search, enterprise integrations, cloud-native engineering, code reviews, AI governance, security guardrails, and stakeholder leadership. The role requires hands-on development, framework evaluation, and translating business needs into production-ready AI platforms.

Lead end-to-end executive and leadership recruiting across business and technology functions, including talent mapping, sourcing, stakeholder partnership, hiring strategy, offer negotiation, and closure. Mentor recruiters, monitor pipelines and hiring metrics, and develop talent strategies for data science, analytics, data engineering, and machine learning roles. The position partners with senior business leaders and supports recruiting growth in a global analytics consulting environment.

Leads analytics consulting engagements using data science and advanced analytics to solve complex business problems. Manages project planning, stakeholder coordination, cross-functional delivery, and analytical solution development. Translates business requirements into actionable insights, presents recommendations to senior stakeholders, supports pricing analytics initiatives, and collaborates with technology teams on cloud-based analytics and data engineering solutions.

Partners with senior leaders and business heads to align HR strategy with business goals. Leads workforce planning, talent and performance management, PIP governance, leadership coaching, HR analytics, employee relations, organizational effectiveness, retention, succession planning, compliance, engagement, and change initiatives across North America.

Lead requirements discovery, documentation, and UAT for enterprise data, automation, and analytics initiatives. Define business domains, map entities to source systems and datasets, conduct SME interviews, develop BRDs and FRDs, establish business rules and data requirements, review technical mappings, assess scope changes, and drive UAT defect resolution and sign-off. Requires healthcare payer expertise, UiPath experience, strong data analysis skills, and the ability to translate business needs into technical specifications.

Leads enterprise HR analytics and AI consulting engagements from discovery through delivery. Translates business challenges into scalable analytics solutions, manages senior client stakeholders, and guides cross-functional data science, data engineering, and business analyst teams. Oversees HR data transformation initiatives, advises on workforce metrics, supports embedding analytics into business processes, and identifies opportunities for solution pitches and account expansion.

Lead analytics consulting engagements for enterprise clients by translating business challenges into AI and analytics solutions. Define agentic AI requirements, workflows, and outcomes; oversee end-to-end delivery; collaborate with technical and global teams; manage stakeholders and projects; apply statistical and machine learning techniques; and help operationalize solutions that deliver measurable business value.

Designs, develops, and maintains scalable Azure data pipelines and ETL/ELT solutions using Python, Scala, Spark, and Azure data services. Responsibilities include optimizing distributed processing, managing data quality and validation, monitoring and troubleshooting pipelines, supporting production deployments, and collaborating with architects, analysts, and cross-functional teams. The role also requires Hadoop experience, Control-M workflow orchestration, SQL performance tuning, and adherence to testing, version control, and CI/CD practices.

Develop and deploy machine learning and predictive models for banking and financial services clients. Responsibilities include feature engineering, model training and validation, credit and risk modeling, fraud detection, production-ready Python development, stakeholder engagement, MLOps collaboration, model monitoring, and communicating technical findings and business impact.

Own enterprise HR analytics and AI consulting engagements from discovery through delivery. Translate business needs into scalable analytics solutions, lead discovery workshops, manage senior client stakeholders, guide cross-functional data science and engineering teams, and support account expansion. Advise on HR metrics including headcount, attrition, turnover, span of control, and workforce productivity while embedding analytics into business processes.

Lead analytics consulting engagements by translating business objectives into analytical roadmaps, defining and governing KPIs, assessing data quality, and conducting hands-on data exploration using SQL and Python. Partner with stakeholders and Power BI developers to design metric structures, semantic models, wireframes, and dashboards that support strategic decision-making.