As an Analytics Engineer at Salmon, you will play a pivotal role in Data Modeling & Transformation (Databricks Silver & Gold Layers). You will work closely with Data Scientists, Engineers, and Business System Analysts to ensure that datasets align with business needs.

Key responsibilities

Data Modeling & Transformation

  • Design, build, and maintain scalable data models in Databricks silver (curated data) and gold (business-ready data) layers.

  • Define clear data contracts between silver and gold to ensure consistency and reliability.

  • Apply best practices for dimensional modeling (star/snowflake schemas) to support analytics and reporting.

Collaboration & Best Practices

  • Partner with data scientists, platform engineers, and business analysts to ensure gold datasets meet business needs.

  • Follow software engineering practices — version control (Git), CI/CD for data pipelines, code reviews, and testing.

  • Contribute to the development of a shared analytics engineering framework (naming standards, reusable templates, testing frameworks).

ETL/ELT Development

  • Develop and optimize transformation pipelines (PySpark/SQL/Delta Live Tables/Databricks Workflows) to process data from bronze → silver → gold.

  • Implement incremental data processing strategies to minimize compute cost and improve pipeline performance.

  • Ensure data quality checks (validations, anomaly detection, deduplication, SCD handling, etc.) are built into transformations.

Data Quality & Governance

  • Establish and maintain data quality metrics (completeness, accuracy, timeliness) for silver and gold tables.

  • Apply data governance standards — consistent naming conventions, documentation, and tagging across datasets.

  • Collaborate with data platform engineers to enforce lineage and observability.

Business Enablement

  • Work closely with analysts and business stakeholders to understand requirements and translate them into gold-layer datasets.

  • Build reusable, business-friendly datasets that power dashboards, self-service BI tools, and advanced analytics.

  • Maintain documentation (data dictionaries, transformation logic, lineage diagrams).

Performance & Optimization

  • Optimize Databricks SQL queries and Delta Lake performance (Z-ordering, clustering, partitioning).

  • Monitor and tune workloads to control compute spend on silver and gold pipelines.

  • Implement best practices for caching, indexing, and incremental updates.

Requirements and expectations

  • Strong SQL expertise

    • Ability to write complex, performant queries (CTEs, window functions, joins)

    • Experience optimizing queries on large datasets

    • Strong understanding of analytical SQL patterns

  • Hands-on experience with dbt

    • Building and maintaining dbt models (staging, intermediate, marts)

    • Writing reusable macros and Jinja templates

    • Implementing tests, documentation, and exposures

    • Working with dbt version control and CI workflows

  • Data Modeling expertise

    • Strong understanding of dimensional modeling (facts, dimensions, star schemas)

    • Ability to translate business requirements into scalable data models

    • Designing metrics and semantic layers for analytics and BI

    • Experience maintaining a single source of truth for business metrics

  • Analytics Engineering mindset

    • Strong focus on data quality, reliability, and consistency

    • Experience working closely with analysts and business stakeholders

    • Ability to balance technical best practices with business needs

  • Production-ready analytics

    • Experience with data testing, monitoring, and debugging

    • Familiarity with ELT pipelines and modern data stack concepts

    • Comfortable working in Git-based workflows