Data Management Analyst

Our client, a leading enterprise retailer with a large and complex technology and data environment, is seeking a Data Management Analyst to support a major finance application modernization and data platform migration initiative. In this role, you will focus on data validation, reconciliation, analysis, and reporting as finance data transitions from legacy mainframe applications to a modern Google Cloud Platform (Google Cloud Platform)-based data environment. You will work across engineering, product, finance, and business teams to identify data variances, investigate root causes, and document findings that support critical go-live decisions. The ideal candidate brings strong hands-on experience with SQL, big data platforms, ETL/data pipelines, Python or PySpark, and data visualization tools. This role requires someone who can independently investigate complex data issues, clearly explain their approach, and collaborate effectively with both technical and business stakeholders., As a Data Management Analyst, you will play a key role in validating data accuracy and consistency between legacy finance systems and the client’s modern cloud-based data platform. You will develop queries, datasets, reports, and dashboards that identify variances between systems during modernization and parallel operations. You will then partner with product managers, engineers, finance teams, and business stakeholders to investigate those differences, determine root causes, and document known variances., * Develop SQL queries and datasets to validate data between legacy and modern applications and identify data variances.

• Perform source-to-target comparisons, data reconciliation, duplicate detection, error and reject validation, and audit/control total validation.
• Validate business rules, transformation logic, mappings, data lineage, and data quality requirements.
• Analyze data variances and collaborate with product managers, engineers, finance teams, business stakeholders, and legacy system teams to determine root causes.
• Develop reports and dashboards using data visualization tools to illustrate data variances, trends, and reconciliation results.
• Build data variance trend dashboards to support stakeholders in establishing tolerance thresholds and making go-live decisions.
• Create reconciliation reports, validation summaries, exception reports, defect logs, and business sign-off documentation.
• Document known data variances, their identified root causes, and applicable resolutions or explanations.
• Support data validation activities during parallel operation of legacy and modern finance applications.
• Participate in Agile delivery activities, including sprint ceremonies, defect triage, user story review, and iterative delivery.
• Communicate findings clearly to both technical and non-technical stakeholders.
• Work independently through complex or unclear data problems while seeking clarification and collaborating with stakeholders as appropriate.

Success in this position requires more than high-level familiarity with data technologies. You should be comfortable working directly with large datasets, navigating ambiguous problems independently, explaining the reasoning behind your technical decisions, and asking thoughtful questions to establish a clear understanding of business and technical requirements., * 5+ years of professional experience in a data analytics or data management role.

• 5+ years of hands-on SQL experience working with large-scale databases or big data platforms such as Google BigQuery, Hadoop, or comparable technologies.
• 3+ years of experience with ETL and data pipeline tools, such as Airflow, Informatica, dbt, or similar technologies.
• 3+ years of experience working within an Agile team environment.
• Hands-on experience with Python and/or PySpark for data processing, movement, and analysis.
• Experience with data visualization and reporting tools such as Power BI, Looker, and LookML.
• Demonstrated experience developing dashboards, performing KPI reconciliation, validating semantic layers, and conducting data analysis.
• Strong experience with data validation, source-to-target reconciliation, variance detection, duplicate detection, reject/error validation, and audit/control total validation.
• Experience validating data against defined business rules and transformation logic.
• Ability to analyze mapping documents, reporting requirements, data transformation logic, data lineage, and data quality rules.
• Experience creating reconciliation reports, validation summaries, exception reports, defect logs, and business sign-off documentation.
• Strong analytical and problem-solving skills with the ability to independently investigate complex or ambiguous data issues.
• Strong communication and collaboration skills with the ability to work across engineering, product, finance, business, and legacy system teams.
• Ability to clearly explain technical approaches, findings, considerations, and decision-making processes., * Previous experience working within the retail industry, particularly with large-scale enterprise data.
• Experience working with data related to finance, supply chain, merchandising, inventory, stores, sales, digital, product, or customer domains.
• Experience with Google Cloud Platform (Google Cloud Platform) data environments, including Data Lake or Lakehouse architectures.
• Familiarity with SAP S/4HANA data structures.
• Finance-domain reporting or dashboard development experience.
• Experience working in Agile delivery environments using user stories, acceptance criteria, sprint ceremonies, defect triage, and iterative delivery.

Dahl Consulting is proud to offer a comprehensive benefits package to eligible employees that will allow you to choose the best coverage to meet your family’s needs. For details, please review the DAHL Benefits Summary: .