Data Lead Manager

Position: Data Lead Manager 
Location: Jersey City, NJ (Onsite)

In-Person Interview is must 
Strong expertise in Informatica, Snowflake, Python, Oracle PL/SQL, and reporting/BI tools to deliver scalable data solutions and lead project execution, with proven delivery experience across Capital Markets corporate functions, securities, and front-to-back-office.

Required Skills and Experience:
Technical: Snowflake, Oracle Pl/SQL, DBT, Python, any BI reporting tool
Strong hands-on experience with Snowflake, including data modeling, performance optimization, and cost management.
Advanced proficiency in Python for data processing, automation, and integration.
Solid experience in Oracle PL/SQL, including complex queries, procedures, and performance tuning.
Experience with at least one enterprise reporting or BI tool.
Proven leadership skills with experience leading teams and owning project delivery.
Strong customer interaction and stakeholder management skills.
Experience driving governance forums and executive-level status reviews.
Exposure to Generative AI, including use cases, tools, or solution implementation, is highly desirable.
This role requires a strong balance of technical depth, leadership capability, and customer-facing skills to successfully deliver high-impact data and analytics solutions

Key Responsibilities:
Design, develop, and optimize data solutions using Snowflake, Python, and Oracle PL/SQL.
Develop and maintain reports and dashboards using standard BI/reporting tools to support business decision-making.
Lead end-to-end project deliverables, ensuring quality, timeliness, and alignment with customer requirements.
Drive and manage weekly and monthly governance calls, including status reporting, risk management, and stakeholder communication.
Ensure all project tasks and milestones are completed on time with high-quality standards.
Provide technical leadership and mentorship to the team, reviewing designs and code to ensure best practices.
Engage directly with customers to understand business needs, translate them into technical solutions, and manage ongoing interactions.
Collaborate with cross-functional teams to ensure smooth delivery and adoption of solutions.
Apply Generative AI concepts and tools where relevant to enhance data engineering, analytics, automation, and productivity use cases.
Continuously identify opportunities for process improvement, automation, and innovation.