Lead Data & Analytics Architect (Power BI)

Lead Data & Analytics Architect (Power BI)

100% remote 

9-12 month Contract

70.00-85.00 USD w2 or C/C

Overview:
Client is  seeking a deeply technical, hands-on Consultant to serve as an Individual Contributor (IC) Team lead on our Data & Analytics team. This person will help lead data architecture and reporting modernization across the team by pairing strategic thinking (architecture, patterns, standards, roadmap) with strong execution. This is a 2 step project  A. The immediate priority migrate / deliver a high volume of SSRS report conversions—migrating reports from T-SQL stored procedure-based datasets to an SSAS Tabular semantic model using DAX.  While not the lead project lead, but will be an active contributor by picking up a subset of reports end-to-end while also helping develop the Snowflake data warehouse and SSAS Tabular semantic layer to support ongoing reporting needs (Power BI ) Beyond delivery, this role is expected to help shape our broader approach to the data warehouse and reporting strategy (including Snowflake), while raising team capability through modern engineering practices and effective use of AI tooling.

Responsibilities
Immediate Delivery- Migration

1.      Must have 3-5 years of prior experience leading an SSRS conversion effort by taking ownership of a subset of reports end-to-end: redesign datasets, migrate report logic to SSAS Tabular, and implement DAX-based querying patterns. (Power BI)

a.      Developing and refactoring enterprise reports in SQL Server Reporting Services (SSRS).

b.      SME with SSAS Tabular models and strong proficiency writing and tuning DAX queries.

c.      Expert-level SQL/T-SQL skills, including stored procedures, performance tuning, and debugging complex data logic.

d.      Experience implementing and troubleshooting SSRS parameters, datasets, and standard/data-driven subscriptions.

2.      Must have 3-5 years of prior experience leading existing SSRS reports currently pointing to T-SQL stored procedures into semantic-model-based reports, ensuring functional parity and improved maintainability.

3.      Must have 3-5 years of prior experience designing, developing, and optimizing SSRS reports, including parameters, interactive features, and layout standards for consistent user experience.

4.      Configure and support standard and data-driven subscriptions, scheduling, and

5.      Drive performance and reliability: tune DAX, model design, and query patterns; identify bottlenecks and recommend remediation.

Longer-Term Priorities-Scale, Architecture, Modernization

1.      Act as an IC architecture lead across the Data & Analytics team: define patterns/standards for reporting, semantic modeling, and warehouse consumption; contribute to roadmaps and target-state design.

a.      Experience with Snowflake (or other modern cloud data warehouses) and associated development patterns (transforms, performance, cost awareness).

b.      Background in dimensional modeling (Kimball, Facts/Dimensions, Star Schemas) and aligning reporting/semantic layers to those models.

c.      Experience building or modernizing data warehouse pipelines and/or ELT processes.

d.      Experience supporting Azure SQL Database in an operational (OLTP) context (tables, views, stored procedures, triggers) and 3NF data modeling principles.

e.      Experience with SQL Projects and deployment automation; familiarity building CI/CD with GitHub Actions.

2.      Contribute to evolution of the data warehouse and data models (e.g., Snowflake and dimensional modeling), ensuring reporting and semantic layers align to governed, performant schemas.

3.      Operate in a DevOps mindset: source control, code reviews, deployment automation, documentation, and repeatable delivery practices.

4.      Adopt an AI-first approach in the data space: proactively use AI/LLM tools to accelerate analysis, development, testing, and documentation; identify repeatable workflows to automate; and set a clear expectation of step-change productivity (e.g., 10×+) while maintaining data quality and governance.

5.      Raise the technical bar: mentor teammates through pairing/reviews, share reusable templates/utilities, and demonstrate effective use of AI-assisted development to improve throughput and quality.

6.      Create and maintain clear documentation of report logic, model dependencies, operational runbooks, and handoff materials.