Data Analyst - Data Integration & Enablement

Data Analyst – Data Integration & Enablement Our client is seeking a motivated Data Analyst – Data Integration & Enablement to join our team supporting a major financial services client. In this role, you will help build and strengthen the trusted data foundation that supports financial and regulatory reporting, enterprise analytics, and data-driven innovation. This is an excellent opportunity for a data professional who enjoys working at the intersection of business, data, and technology. You will use SQL and Python to analyze complex datasets, profile source systems, define data integration requirements, validate data quality, and help ensure information is accurate, traceable, and ready for downstream reporting and analytics. You will collaborate with business stakeholders, data engineers, architects, and analysts to integrate data across modern enterprise platforms, including the Enterprise Data Warehouse, Data Lake, Data Mesh, and Operational Data Store (ODS). You will also contribute to data modernization initiatives and gain exposure to technologies such as dbt, Databricks, Google Cloud Platform (GCP), and AI/GenAI-enabled solutions. Your future duties and responsibilities include: Partner with business and technology stakeholders to understand business needs and translate them into clear, actionable data integration, reporting, and analytics requirements. Profile and analyze source systems, data structures, and business rules using SQL and Python to identify data relationships, anomalies, quality issues, and integration requirements. Analyze and document source-to-target data flows supporting the Enterprise Data Warehouse, Data Lake, Data Mesh, ODS, and downstream reporting and analytics solutions. Define and maintain source-to-target mappings, transformation rules, metadata, business definitions, and data lineage to improve data transparency, traceability, and regulatory compliance. Develop and execute comprehensive data validation and testing activities, including data reconciliation, transformation validation, test cases, and data quality controls. Develop and enhance automated processes using SQL, Python, and modern data technologies to improve the efficiency of data profiling, validation, reconciliation, and reporting. Collaborate with data engineers, architects, business analysts, and end users to troubleshoot data issues, perform root cause analysis, and ensure delivered solutions meet business and functional requirements. Contribute to data modernization and continuous improvement initiatives using technologies and approaches such as dbt, Databricks on GCP, Data Mesh, and AI/GenAI. Promote data quality and documentation best practices, share knowledge across the team, and provide guidance or mentorship to team members as appropriate. Required qualifications to be successful in this role include: Bachelor's degree in Data Analytics, Computer Science, Information Systems, Business, or a related discipline, along with 2+ years of experience in data analysis, data integration, business intelligence, or a related data-focused role. Strong hands-on SQL skills, including complex queries, joins, aggregations, data profiling, transformation analysis, reconciliation, and data validation. Working proficiency in Python for data analysis, automation, data quality validation, and data integration activities. Experience analyzing source and target data models and documenting source-to-target mappings, transformation logic, metadata, data lineage, and business rules. Solid understanding of data quality, data testing, reconciliation, ETL/ELT, data integration, and troubleshooting within enterprise data environments. Strong analytical and problem-solving skills, with the ability to investigate complex data issues, identify root causes, and recommend solutions. Strong requirements gathering, documentation, and stakeholder communication skills, with the ability to translate business needs into actionable data requirements. Ability to collaborate effectively with both business and technology teams in a complex enterprise environment. Desired qualifications include: Exposure to modern data platforms and technologies such as Databricks, dbt, GCP, Enterprise Data Warehouses, Data Lakes, Data Mesh, and Operational Data Stores (ODS). Experience supporting data environments within financial services, banking, regulatory reporting, or another highly regulated industry. Experience coordinating project activities, supporting workstreams, or mentoring team members. Exposure to AI/GenAI concepts, AI-enabled data solutions, or prompt engineering. Employment Practices: VLink is an equal opportunity employer committed to fostering an inclusive environment where diversity is celebrated. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status. Employment is contingent upon successful completion of a background check. This job application process may use AI-powered tools to assist in screening and evaluating applications based on objective, job-related qualifications. AI is used solely to support the recruitment process and all final hiring decisions are made exclusively by our human recruitment team. Applicant information will be handled in accordance with VLink's privacy policy. Vlink