Sr. Manager, Enterprise Data & Analytics

Automation Anywhere•San Jose, CA

2d•$200,000 - $230,000•Hybrid

About The Position

Automation Anywhere is seeking a Senior Manager to lead the delivery of scalable enterprise data, analytics, and AI solutions through team leadership, technical direction, and business partnership. The role translates business priorities into a data and analytics roadmap, guides the architecture of the enterprise data warehouse (EDW) and data lake ecosystem, and oversees trusted data products and reliable ingestion and transformation pipelines. As the primary IT partner for business stakeholders, the Senior Manager aligns priorities, communicates progress and trade-offs, and drives adoption and measurable business value. The position provides technical oversight and targeted hands-on guidance across Informatica, Google BigQuery, and Tableau. The Senior Manager leads and develops data engineering, analytics, and architecture professionals; owns the target-state EDW, data lake, and analytics architecture; and directs roadmap, resource planning, integration, BigQuery engineering, transformations, and analytics delivery. The role establishes data quality and governance, security and compliance practices, and the prioritization and delivery of AI-enabled solutions using governed data. It also owns service health for pipelines and dashboards, leads operational improvement, and serves as a trusted IT advisor to business stakeholders. This is a hybrid role requiring regular onsite workdays at the San Jose, California office. Other U.S. locations may be considered. The position reports to the VP, Business Applications. The base salary range is $200,000–$230,000 per year, with eligibility for a discretionary bonus, equity, and medical and other benefits.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Analytics, or a related field, or equivalent practical experience.
10+ years of experience across data engineering, business intelligence, data analysis, data warehousing, or solution architecture, including technical-team management and delivery of cross-functional data and analytics initiatives.
Experience hiring, coaching, delegating, developing teams, and being accountable for delivery outcomes.
Experience owning data and analytics roadmaps, prioritizing competing business demands, planning team capacity, managing cross-team dependencies, and developing resource and investment recommendations.
Ability to explain delivery, risk, and cost trade-offs to business and IT leadership.
Hands-on experience with Informatica products and enterprise ETL/ELT design, development, deployment, and support.
Strong experience with Google BigQuery, SQL, cloud data warehousing, data lake concepts, data modeling, and query optimization.
Demonstrated experience developing Tableau dashboards, semantic layers, calculated fields, data sources, row-level security, and performance-optimized visualizations.
Experience building AI-enabled solutions on enterprise data warehouse or data lake platforms, including secure access to governed data for AI models, agents, or applications.
Hands-on experience designing and implementing RBAC and RLA security controls across BigQuery, Tableau, and enterprise data products.
Strong knowledge of dimensional modeling, star and snowflake schemas, slowly changing dimensions, master and reference data, and data lifecycle management.
Experience integrating structured and semi-structured data from SaaS applications, relational databases, APIs, and files.
Proficiency in SQL and at least one scripting or programming language such as Python.
Experience with data quality frameworks, metadata management, data lineage, testing, CI/CD, and production support.
Strong communication and influencing skills, including explaining technical concepts to business audiences, translating requirements into scalable solutions, presenting recommendations to senior leaders, facilitating decisions, and building alignment across teams.
Ability to serve as the face of IT for business teams, build trusted stakeholder relationships, lead recurring working sessions, and clearly communicate solution strategy, trade-offs, risks, and delivery status.

Responsibilities

Lead, hire, onboard, coach, and develop data engineering, analytics, and architecture team members; set goals and ownership, delegate work, and build team capability and succession readiness.
Own the target-state EDW, data lake, and analytics architecture, guide its evolution, and review data models, integration patterns, security controls, metadata, lineage, and nonfunctional requirements.
Own the enterprise data and analytics roadmap and backlog; prioritize initiatives with stakeholders, align commitments to capacity and dependencies, recommend resources and investments, and review platform consumption, licensing, and operating costs.
Lead the design and delivery of batch and near-real-time Informatica pipelines that ingest data from enterprise applications, databases, APIs, files, and third-party platforms; establish reusable patterns and reliability and freshness expectations.
Direct development and optimization of curated data layers, dimensional models, data marts, and lakehouse-style datasets in Google BigQuery, including standards for partitioning and clustering and trade-offs among performance, scalability, and cost.
Establish reusable data transformation standards and oversee standardization, reconciliation, and orchestration across raw, trusted, and consumption layers; review high-risk changes and ensure logic is tested, documented, and maintainable.
Partner with business leaders to define KPIs and analytical priorities and direct delivery of governed Tableau dashboards, reports, and self-service analytics; align acceptance criteria and review adoption, usability, and business impact.
Elicit and document requirements, profile source data, conduct exploratory analysis, identify trends and anomalies, and translate findings into actionable recommendations.
Establish data quality rules, monitoring, exception handling, lineage, reference data, business definitions, stewardship processes, and auditability.
Apply role-based access, least-privilege principles, sensitive-data protections, retention requirements, and applicable privacy and regulatory controls.
Lead prioritization, design, and delivery of AI-enabled solutions using governed EDW and data lake assets; guide implementation of RBAC and RLA, and oversee evaluation, production readiness, and ongoing monitoring.
Serve as the primary IT partner and trusted advisor to business stakeholders; establish governance cadence, align priorities, facilitate decisions, manage expectations, communicate delivery status, risks, and trade-offs, and coordinate dependencies and escalations.
Own service health for data pipelines and dashboards, agree service expectations, monitor performance, troubleshoot production issues, lead incident escalation and root-cause reviews, and prioritize reliability improvements through automation, testing, and observability.
Lead design reviews, create architecture standards and technical documentation, mentor team members, and collaborate with application, cloud, security, and business teams.