Summary

The Senior Data Specialist is a highly autonomous, client-facing role responsible for turning fragmented, raw data into high-value operational assets. Unlike a traditional support role, you will act as the primary technical lead on client engagements. You will work directly with customer stakeholders to audit their data ecosystems, define requirements, and architect complex SQL-based transformation logic. Your goal is to bridge the gap between "messy" source data and the structured data products required for strategic initiatives like Customer 360 and large-scale migrations. You will be responsible for sourcing this data into the a data management SAAS platform and "humanizing" it by assigning precise semantic types (e.g., first_name, last_name, email_address, phone_number). Your work ensures that raw, virtualized data is not just moved, but transformed into intelligent data products ready for strategic initiatives like Customer 360 and large-scale migrations.

Key Responsibilities

Discovery & Consulting: Lead discovery sessions with clients to map their business processes to their underlying data structures.

Independent Architecture: Act as the primary technical point of contact; define the data transformation strategy without needing oversight from a Solution Architect.

Advanced SQL Development: Design, write, and optimize sophisticated SQL queries.

This includes the heavy use of:

  * Complex multi-way JOIN logic.

  * Common Table Expressions (CTEs) for modular query design.

  * Window functions and advanced aggregations for data profiling.

Data Product Delivery: Use the Data Management SAAS Platform to build, test, and deploy production-ready data products that meet strict client specifications.

Client Management: Manage the technical relationship with the client, including three weekly progress calls and ad-hoc troubleshooting.

Requirements & Qualifications

Expert-Level SQL: Deep proficiency in writing complex, performance queries against large datasets. You should be comfortable navigating "spaghetti" schemas and creating order from chaos.

Autonomy: Proven ability to manage a project from start to finish with minimal supervision.

Communication: Ability to translate technical data challenges into business-friendly language for client stakeholders.

Technical Environment: Experience with the Data Management SAAS Platform or similar data virtualization/ETL tools is a significant plus.