Director - Subledger Integration Lead

Job Description

We are looking for a highly motivated Director – Data Integration Subledgers who will lead and provide strategic direction for the Finance and Risk Data Platform. The Director – Data Integration Subledgers identifies data sources, extracts key data, transforms it into actionable insights / standard data models, and monitors data quality to meet the organization's information system needs and requirements. This role applies extensive knowledge and practices to bridge the gap between business and technology by delivering critical insights and analysis to shape strategy, evolve data connectivity, and provide insights into data transformation.

• Lead a team of analysts within the Data Engineering group, including project oversight, coaching and professional development.
• Lead other tasks on transformation, data governance, system infrastructure, analytics tool evaluation, and other cross‑team functions on an as‑needed basis. Ability to inspire highest level of quality/rigor/thought leadership in the complete data lifecycle including gathering, transforming, reporting and analytics of large data sets.
• Build best‑in‑class integration pipelines from data to the ERP platform.
• Guide the team in development of actionable solutions and creation of deliverables that effectively communicate findings and recommendations.
• Leverage big data and cutting‑edge data mining techniques, providing thought leadership in analytic techniques and business applications to unlock the value of the bank’s unique data set.
• Lead development of assets supporting scalable analytic approaches capable of being leveraged by data scientists and analysts globally.
• Make recommendations and build use cases on new sources of value by addressing the biggest gaps in our data sources in relation to revenue potential.
• Evangelize new analytic approaches for processing big data through internal training, documentation, and by leading technical sharing sessions.
• Utilize Hadoop and related query engines such as Hive, Databricks, Snowflake, to perform advanced data mining and analysis.
• Research industry metrics and business context and bring this context to bear in analyses. Find opportunities to create and automate repeatable analyses or build self‑service tools for business users.
• Direct the execution of medium to large analytic projects based on business requirements and desired outcomes.
• Define detailed analytic scope and methodology and create architecture plans for data assets.

• Apply problem‑solving techniques and business acumen to derive business insights toward simple and complex business objectives.
• Understand the business in focus areas to ensure analytical insights are properly understood in business context.
• Utilize best practices to operationalize analytical work building processes and outputs that are agile, efficient, sustainable, and resilient.

• Minimum of 7+ years of analytical experience in applying solutions to business problems in relevant fields such as analytics, business consulting, or other data‑driven functions.
• Advanced quantitative, qualitative, analytical, problem‑solving, and critical thinking skills.
• Advanced knowledge of databases and engineering concepts with hands‑on experience with one or more data analytics/programming tools such as Hive, SQL, Spark, Python.
• Experience utilizing querying, automation, and big data technologies (Python, SQL, Spark, Teradata, Hadoop, Snowflake, Databricks) to produce repeatable insights.
• Exceptional storytelling skills, with a track record of translating complex data into compelling business insights.
• Expertise in the application of predictive modeling and machine learning techniques.
• Knowledge of the big data engineering stack including Hadoop, Spark, Kafka, Snowflake, Databricks and other related components.
• Solid understanding of finance business processes such as balance‑sheet reporting, profit and loss reporting, capital reserve reporting, capital markets products and processes, and regulatory reporting.
• Detail‑oriented, ensuring the accuracy and reliability of complex analyses and deliverables.
• Ability to…