Sr. Consultant, Analytics Engineer
Role details
Job location
Tech stack
Job description
We are looking for a Sr. Consultant, Analytics Engineering to join our growing team of experts. This position sits at the intersection of data engineering and analytics, focused on transforming raw, ingested data into trusted, well-modeled, and well-documented assets that power decision-making, BI, and downstream AI/ML use cases.
The Sr. Consultant, Analytics Engineering will own the design and delivery of dimensional and analytical data models, semantic layers, testing and observability frameworks, and CI/CD for analytics workflows. You will partner closely with Data Engineers (who own ingestion and platform), BI Developers, Analysts, and client stakeholders to translate business requirements into durable, reusable, version-controlled data products. You will lead modeling decisions on customer engagements and mentor junior analytics engineers and analysts on dbt, modeling patterns, and analytics best practices.
The right candidate is excited about software engineering rigor applied to analytics: modular SQL, automated testing, peer review, lineage, and treating data models as products with SLAs and consumers.
As of April 2025, Hakkoda has been acquired by IBM and will be integrated in the IBM organization. Your recruitment process will be managed by IBM. IBM will be the hiring entity.
This role can be performed from anywhere in the US.
Required technical and professional expertise
Requirements
Bachelor's degree in engineering, computer science, analytics, statistics, or equivalent practical experience.
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5+ years in analytics engineering, data modeling, BI engineering, or closely related roles delivering production analytics on cloud data platforms.
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Expert-level SQL: complex window functions, CTEs, query optimization, and warehouse-specific tuning (Snowflake preferred; Databricks, BigQuery, or Redshift acceptable).
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Production experience building, owning, and operating dbt projects (dbt Core or dbt Cloud), including macros, packages, Jinja templating, incremental models, snapshots, and exposure