Analytics Engineer

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:

• 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

Nice-to-Have Qualifications

• Python

  • Writing data transformations or utilities

  • Familiarity with pandas or similar libraries

  • Using Python for data quality checks or automation

• Databricks

  • Experience working in Databricks notebooks or workflows

  • Understanding of Databricks architecture and job orchestration

• Apache Spark

  • Experience with Spark SQL or PySpark for large-scale data processing

  • Understanding of distributed data processing concepts

• Delta Lake

  • Knowledge of Delta Lake tables, ACID transactions, and time travel

  • Experience designing reliable, incremental data pipelines

• Cloud data platforms

  • Experience with modern cloud data environments (Databricks, AWS / GCP / Azure)

  • Familiarity with data lakes and lakehouse architectures

Bonus

• Experience supporting BI tools (Looker, Tableau, Power BI, etc.)

• Strong documentation and communication skills

• Experience scaling analytics in fast-growing environments

What we offer

• Passionate international team spanning the globe

• Rapid professional growth. Merit (and merit only) rules the day

• Reward for performance and long-term success of Salmon

• Fast track to grow internationally

• New office in Manila, Philippines

• Relocation support for eligible candidates

• Remote and hybrid options

• Medical insurance, health and wellness benefits

• Program of events and activities both online and in person