BI Engineer - AWS -- REMOTE WORK -- 66019

Must Haves:

-AWS

- Glue

- Redshift

-S3

- Conceptual knowledge of databricks or snowflake

Current State Assessment

• Review of Redshift usage (workload types, performance, cost, elasticity).

• Review of BI cube usage (architecture, refresh cycles, latency, query response time, governance).

• Mapping of data flow from ingestion to analytics.

Benchmarking Against Best Practices

• Industry reference architectures for large-scale analytics.

• Cost-performance benchmarking.

• Evaluation of emerging approaches (Lakehouse, serverless analytics).

Alternatives Assessment

• Evaluate feasibility and trade-offs of keeping Redshift vs. migrating to another platform (Snowflake, Databricks SQL, BigQuery).

• Analysis of potential cube alternatives (semantic layers in Looker, Power BI Datasets, dbt metrics layer).

Recommendation Framework

• Scenarios: Stay/Optimize, Hybrid, or Migrate.

• Cost, complexity, and scalability comparison.

• Roadmap for transition (if applicable).

Client's approach includes a focused assessment of current BI and data infrastructure, platform benchmarking, and evaluation of cube alternatives. This will result in actionable recommendations and a roadmap aligned to business objectives.

Discovery and Data Collection:

• Workshops with BI, Data Engineering, Finance, and Analytics teams.

• Review Redshift usage patterns (query logs, concurrency scaling, WLM configurations).

• Analyze BI cube refresh frequency, load, and performance.

Benchmarking and Alternatives Analysis

• Compare Redshift vs. Databricks vs. Snowflake (performance, elasticity, pricing models).

• Evaluate cube alternatives (semantic layers, direct query, in-memory models).

Recommendations and Actionable Roadmap

• Consolidate findings.

• Present stay/optimize/migrate scenarios.

• Provide roadmap and business case.