Job Description

JOB SUMMARY: The Data Engineer II supports the Bank’s business intelligence and enterprise reporting capabilities by designing, developing, enhancing, and maintaining reliable reporting and analytics solutions. The role partners with business and technology stakeholders to translate reporting requirements into actionable dashboards, operational reports, and governed data products using the Bank’s business intelligence platforms, including SQL Server Reporting Services (SSRS), Tableau, and Oracle Business Intelligence Enterprise Edition (OBIEE). This position is responsible for report development, data analysis, validation, troubleshooting, performance optimization, documentation, and production support to ensure that accurate, timely, secure, and consistent information is available to authorized users.

ESSENTIAL DUTIES AND RESPONSIBILITIES

• Designs, develops, and maintains enterprise reporting solutions as a primary responsibility of the Data Engineer role.
• Supports the reporting and analytics needs of business units and internal stakeholders.
• Designs, develops, tests, deploys, and maintains enterprise reports, dashboards, visualizations, and analytics solutions using SSRS, Tableau, OBIEE, and other approved business intelligence tools.
• Gathers and analyzes business requirements and translates them into technical specifications, report layouts, data definitions, calculations, filters, prompts, and acceptance criteria.
• Develops and optimizes SQL queries, stored procedures, views, datasets, and reporting data sources that support accurate and efficient business intelligence solutions.
• Creates and maintains operational, management, regulatory, and ad hoc reporting solutions that meet established business, security, and governance requirements.
• Builds and supports SSRS reports, shared datasets, data sources, parameters, schedules, and subscriptions, including data-driven subscriptions, where appropriate.
• Develops and maintains Tableau dashboards and published data sources, applying effective visualization standards, calculations, filters, and performance optimization practices.
• Validates report results and reconciles data to authoritative sources; investigates and resolves data discrepancies, calculation issues, failed report executions, and production incidents.
• Monitors report usage, execution performance, refresh schedules, and distribution processes, and recommends improvements that enhance reliability, scalability, and user experience.
• Applies source control, peer review, testing, release management, and documentation standards throughout the business intelligence development lifecycle.
• Partners with data engineering, database, application, governance, security, and business teams to ensure reporting solutions leverage approved, reliable, and well-defined data sources.
• Maintains technical and user documentation, including data mappings, report specifications, business rules, support procedures, and knowledge transfer materials.
• Provides production support and responds to business inquiries related to report functionality, data interpretation, user access, and scheduled distribution.
• Promotes governed self-service analytics by educating users on available reporting capabilities, appropriate data usage, and approved enterprise reporting practices.
• Leverages AI-enabled capabilities to modernize enterprise reporting products and services by identifying opportunities to enable self-service reporting and near real-time business insights.
• Utilizes modern data preparation, integration, automation, and AI-enabled metadata management tools and techniques.
• Senior-Level Responsibilities:
• Works with data science teams, business analysts, and other stakeholders to refine data requirements for strategic data and analytics initiatives.
• Proposes innovative and scalable data ingestion, preparation, integration, transformation, and operationalization techniques.
• Trains and mentors data scientists, data analysts, line-of-business users, and other data consumers on data preparation, integration, and pipeline development practices.
• Ensures that data consumers use provisioned data responsibly by supporting data governance, compliance, security, and data quality initiatives.
• Participates in evaluating, promoting, and curating analytical content for inclusion in the enterprise data catalog to support governed reuse.
• Serves as a data and analytics evangelist by promoting available data and analytics capabilities to business leaders and educating stakeholders on leveraging those capabilities to achieve business objectives.
• Adheres to and complies with applicable, federal and state laws, regulations and guidance, including those related to anti-money laundering (i.e. Bank Secrecy Act, US PATRIOT Act, etc.).
• Adheres to Bank policies and procedures and completes required training.
• Identifies and reports suspicious activity.

QUALIFICATIONS

Education

• Bachelor's Degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field required
• Master's Degree An advanced degree in computer science preferred
• Information science (MIS), data management, information systems, information science (post-graduation diploma or related) or a related quantitative field or equivalent work experience preferred
• Combination of IT skills, data governance skills, analytics skills and banking domain knowledge with a technical or computer science degree preferred

Experience

• 4 years of work experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data engineering responsibilities and tasks required
• 4 years of experience working in cross-functional teams and collaborating with business stakeholders in the banking business domain, in support of a departmental and/or multi-departmental data management and analytics initiative required
• Strong experience with advanced analytics tools for object-oriented/object function scripting using languages such as R, Python, Java, and Scala required
• Strong experience with popular database programming languages including SQL and PL/SQL for relational databases and certifications on upcoming NoSQL/Hadoop oriented databases like MongoDB and Cassandra for nonrelational databases required
• Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies These should include ETL/ELT, data replication/CDC, message-oriented data movement, API design and access and upcoming data ingestion and integration technologies such as stream data integration, CEP and data virtualization required
• Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping to move them in production required
• Basic experience working with popular data discovery, analytics and BI software tools like Tableau, and OBI for semantic-layer-based data discovery required
• Strong experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms required
• Basic experience in working with data governance teams and specifically business data stewards and the CISO in moving data pipelines into production with appropriate data quality, governance and security standards and certification required

Licenses and Certifications

• AWS

Knowledge, Skills, and Abilities

• Strong ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata and workload management. The ability to work with both IT and business in integrating analytics and data science output into business processes and workflows.
• Demonstrated ability to work across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, AWS Elastic Container Service and others.
• Proficiency in agile methodologies and the capability of applying DevOps and increasingly DataOps principles to data pipelines to improve the communication, integration, reuse and automation of data flows between data managers and consumers across an organization
• Deep domain knowledge or previous experience working in the banking business would be a plus.
• Some experience in working with SQL on Hadoop tools and technologies including HIVE, Impala, Presto and others from an open-source perspective and Hortonworks Data Flow (HDF), Dremio, Informatica, Talend among others from a commercial vendor perspective.

Additional Information

• Candidates residing in locations within BankUnited's footprint may be given preference.