Sr. Consultant, Analytics Engineer

Introduction

At IBM, work is more than a job - it's a calling:
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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.

• Bachelor's degree in engineering, computer science, analytics, statistics, or equivalent practical experience.
• 5+ years in analytics engineering, data modeling, BI engineering, or closely related roles delivering production analytics on cloud data platforms.
• Expert-level SQL: complex window functions, CTEs, query optimization, and warehouse-specific tuning (Snowflake preferred; Databricks, Big Query, or Redshift acceptable).
• Production experience building, owning, and operating dbt projects (dbt Core or dbt Cloud), including macros, packages, Jinja templating, incremental models, snapshots, and exposures.
• Strong command of dimensional modeling (Kimball star/snowflake schemas, slowly changing dimensions, conformed dimensions) and pragmatic application of OBT, normalized, and Data Vault patterns where appropriate.
• Demonstrated ability to translate ambiguous business requirements into a layered modeling architecture (staging, intermediate, marts, semantic) with clear ownership, naming conventions, and documentation.
• Experience defining and governing metrics in a semantic layer (dbt Semantic Layer / Metric Flow, LookML, Cube, or equivalent), including metric definitions, dimensional consistency, and downstream BI exposure.
• Hands‑on experience implementing data quality and testing frameworks: dbt tests (generic and singular), data contracts, freshness checks, anomaly detection, and lineage-based impact analysis.
• Git-based workflows for analytics: feature branching, pull requests, peer review, and CI/CD pipelines (Git Hub Actions, Git Lab CI, Azure Dev Ops, or similar) for dbt projects.
• Working knowledge of orchestration patterns and tools used to schedule transformation workloads (dbt Cloud, Airflow, Dagster, Prefect, or platform‑native schedulers).
• Python scripting for analytics tooling, automation, and lightweight transformations where dbt/SQL is not the right fit.
• Cloud experience on AWS (Azure, GCP are nice to have as well).
• Experience integrating modeled data with BI and consumption tools (Tableau, Power BI, Looker, Sigma, Hex, Mode) and partnering with BI developers on semantic alignment.
• Track record of leading modeling decisions on client engagements, including reviewing and approving model designs from other engineers.
• Mentorship of junior analytics engineers and analysts on modeling patterns, dbt best…