Senior Data Analyst

Senior Data Analyst

We are seeking a Senior Data Analyst to support a portfolio of enterprise data management and analytics initiatives focused on improving how product, customer, contract, pricing, usage, and billing data is understood across the organization. The company currently operates across multiple front-office, middle-office, and back-office platforms that do not consistently identify products, features, customers, or contractual terms in the same way. A product may have one name in CRM, another representation in pricing, and a different description on a contract or invoice. This role will help analyze those disconnected datasets, establish the appropriate business relationships, and create cross-reference models that explain what was sold, priced, contracted, used, and invoiced. This is a hands-on analytical role requiring strong SQL, Python, Databricks, data modeling, data profiling, normalization, and validation skills. The analyst will also spend significant time working with business and operational stakeholders to formulate use cases, capture requirements, validate findings, and obtain agreement on proposed data mappings. The ideal candidate is not simply a report writer. This person can independently explore large datasets, identify patterns and inconsistencies, frame ambiguous business questions, and produce curated, validated datasets that can be used by enterprise data, analytics, automation, and AI teams.

Key Responsibilities

• Enterprise Data Analysis and Mapping
• Query and analyze large datasets across CRM, product, pricing, contract, service, ERP, billing, and other enterprise platforms.
• Profile source data to identify patterns, quality issues, missing relationships, inconsistent terminology, duplicate records, and gaps in data lineage.
• Build cross-reference datasets that map products, features, customers, contracts, pricing structures, platform activity, and invoices across disconnected systems.
• Create translation layers that establish consistent definitions across front-office, middle-office, and back-office applications.
• Help the organization determine whether the product that was sold, priced, contracted, provisioned, and invoiced represents the same underlying offering.
• Identify situations in which products or features exist in one system but do not have a corresponding record in another.
• Analyze data lineage from source systems through downstream reporting, billing, and analytical platforms.
• Data Modeling and Dataset Development
• Use SQL, Python, PySpark, and Databricks notebooks to extract, cleanse, normalize, join, test, and validate enterprise data.
• Develop logical and analytical data models that explain relationships among customers, products, features, contracts, usage, pricing, revenue, and service requests.
• Create curated datasets for consumption by enterprise data integration, data warehouse, analytics, automation, and AI teams.
• Apply relational database concepts, including normalization, dimensional modeling, indexing, partitioning, and fact and dimension structures.
• Evaluate data distribution, skew, partitioning, and join strategies when processing large datasets in Databricks or Spark environments.
• Support data movement and transformation across lake, warehouse, and lakehouse environments.
• Document data definitions, mapping rules, transformation logic, lineage, validation results, and known exceptions.
• Data Quality, Testing, and Governance
• Establish validation rules to confirm that cross-system mappings are complete, accurate, and suitable for business use.
• Perform unit, system, reconciliation, and data-quality testing across source and target datasets.
• Present proposed mappings to business owners and subject-matter experts for review and sign-off.
• Identify governance gaps and recommend controls that prevent unsupported or inconsistently defined products from progressing through sales, pricing, contracting, and billing processes.
• Help establish repeatable processes for creating, modifying, and governing product and reference data.
• Support remediation activities in manageable product groups or Agile increments.
• Maintain traceability from identified business issues through analysis, validation, mapping, remediation, and final implementation.
• Work effectively in an environment where business teams may identify problems without having a defined technical or governance solution.
• Business Analysis and Stakeholder Engagement
• Partner with stakeholders across product, sales, market planning, legal, finance, operations, technology, and enterprise data teams.
• Facilitate discovery sessions to understand how each business area creates, modifies, interprets, and uses data.
• Translate business processes and operational challenges into clear use cases, data questions, requirements, and acceptance criteria.
• Break complex use cases into the individual questions, data components, business rules, and validation steps needed to answer them.
• Communicate analytical findings clearly to both technical and nontechnical audiences.
• Lead working sessions and demonstrations that show how data was mapped, what assumptions were applied, where exceptions remain, and which governance decisions are required.
• Present findings and recommendations to project leadership and steering committees.
• Demonstrate confidence, persistence, and sound judgment when resolving conflicting definitions or securing stakeholder alignment.
• Analytics, Automation, and AI Enablement
• Partner with enterprise data teams to prepare datasets and analytical structures for downstream reporting, natural-language querying, and generative AI capabilities.
• Validate that curated repositories can accurately respond to approved business use cases and questions.
• Support contract digitization initiatives by helping structure data extracted from PDFs and other documents.
• Collaborate with automation teams that move contract and product information into downstream operational systems.
• Evaluate opportunities to use machine learning, fuzzy matching, regular expressions, token matching, or other analytical techniques to improve entity resolution and cross-system matching.
• Help connect contract information to customer, product, pricing, service, revenue, and billing data to improve operational efficiency and revenue visibility.

Expected Focus

The role is expected to balance:

• Approximately 60% hands-on data analysis and dataset development, including querying, profiling, normalization, modeling, validation, lineage analysis, and cross-system mapping.
• Approximately 40% business engagement, including use-case formulation, requirements gathering, discovery meetings, validation sessions, demonstrations, and governance discussions.

The balance may shift as the initiative progresses from discovery and mapping into remediation and business-as-usual data stewardship.

Ideal Candidate Profile: The strongest candidates will be able to demonstrate that they have personally:

• Queried raw data rather than relying exclusively on completed dashboards.
• Combined and reconciled data from multiple systems that used different naming conventions or identifiers.
• Built a cross-reference, mapping table, translation layer, or master dataset.
• Investigated data lineage and explained where data was transformed or lost between systems.
• Identified data-quality problems and worked with business owners to validate the correct answer.
• Used SQL and Python within Databricks or a comparable distributed platform.
• Translated an ambiguous business request into specific data questions, requirements, and validation rules.
• Presented sensitive or disputed data findings to senior stakeholders and helped drive a governance decision.

This is not primarily a Power BI, Excel, or dashboard-development position. Reporting and visualization experience are helpful, but the central requirement is the ability to interrogate complex data, establish relationships across disconnected platforms, and produce accurate, governed datasets. The pay rate for this role ranges between $55-65/hour.