Sr. Consultant, Analytics Engineer

Hakkōda, an IBM Company, is seeking a Sr. Consultant, Analytics Engineer to join their team. This role focuses on transforming raw data into well-modeled assets for decision-making and BI, while leading the design and delivery of analytical data models and mentoring junior engineers.

Responsibilities:

• Own the design and delivery of dimensional and analytical data models, semantic layers, testing and observability frameworks, and CI/CD for analytics workflows
• Partner closely with Data Engineers, BI Developers, Analysts, and client stakeholders to translate business requirements into durable, reusable, version-controlled data products
• Lead modeling decisions on customer engagements and mentor junior analytics engineers and analysts on dbt, modeling patterns, and analytics best practices
• Apply software engineering rigor to analytics: modular SQL, automated testing, peer review, lineage, and treating data models as products with SLAs and consumers

Requirements:

• 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
• 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