Sr. Consultant, Analytics Engineer

Introduction

At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk

Your Role And Responsibilities

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.

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• 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, BigQuery, 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 / MetricFlow, 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 (GitHub Actions, GitLab CI, Azure DevOps, 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 practices, code review standards, and analytics engineering rigor.
• Ability to prepare technical and business-facing artifacts (model design docs, lineage maps, metric catalogs, runbooks) and present to internal and customer stakeholders.
• Track record of sound problem-solving skills and an action-oriented mindset.
• Strong interpersonal skills including assertiveness and ability to build strong client relationships, particularly with analyst and business stakeholders.
• Ability to work in Agile teams.
• Experience hiring, developing, and managing a technical team.
  
  

Preferred Technical And Professional Experience

• Snowflake certifications (SnowPro Core, SnowPro Advanced: Data Engineer or Architect) or dbt certifications (dbt Analytics Engineer, dbt Cloud Developer).
• Experience with reverse-ETL tooling (Hightouch, Census) and operational analytics use cases.
• Experience designing and governing a semantic/metrics layer at scale, including metric versioning, deprecation, and stakeholder alignment across multiple consumers.
• Familiarity with data catalog and observability tooling (Atlan, Alation, Collibra, Monte Carlo, Elementary, Soda) and integrating these with dbt projects.
• Experience supporting AI/ML and feature-store use cases with curated, well-tested analytics datasets.
• Familiarity with data contracts, model SLAs, and treating analytics models as versioned, consumer-facing products.
• Industry experience in financial services, healthcare/life sciences, retail/CPG, or public sector.