[Remote] Analytics Engineer

Note: The job is a remote job and is open to candidates in USA. TrustedTech is looking for an Analytics Engineer to take complete ownership of the data transformation layer that sits at the heart of how they make decisions as a business. The role involves building and maintaining production dbt projects, writing optimized SQL, and participating in the migration of their core platform from Azure Synapse Analytics to Microsoft Fabric.

Responsibilities

• Own and evolve TrustedTech’s dbt project architecture, maintaining a clean separation of staging, intermediate, and presentation mart layers with consistent use of macros, packages, and ref/source patterns throughout
• Write advanced SQL across Azure Synapse Analytics and Microsoft Fabric SQL endpoints, including window functions, CTEs, recursive queries, and performance-tuned logic, with a strong understanding of how query plans behave at scale
• Hold the team’s dbt standards to a high bar: schema tests, singular tests, freshness checks, model documentation, and exposure definitions are not optional; they’re the mandatory baseline for every production model
• Own the centralized semantic layer, defining metrics, dimensions, and business logic in dbt so that Power BI and downstream consumers are always working from consistent, pre-agreed definitions rather than diverging independently
• Conduct regular SQL performance reviews, identifying slow-running models and applying the right database materialization strategy (incremental, table, view, or ephemeral) based on actual usage patterns and data volume
• Operate confidently across both Azure Synapse Analytics and Microsoft Fabric during our platform transition, maintaining existing Synapse pipelines while actively migrating workloads to Fabric Lakehouses, Warehouses, and Data Pipelines
• Build modular dbt models that work cleanly against both Synapse and Fabric SQL endpoints, managing any dialect or adapter differences without creating technical debt that complicates the migration
• Consolidate transactional data from CRM, finance, billing, and operations into a single trusted platform that supports executive reporting today on Synapse, and scales cleanly into Fabric tomorrow
• Define and enforce data quality standards across source systems, working with owning teams to capture the data points that matter most for reporting fidelity
• Partner with Engineering and IT to onboard new data sources into the warehouse through well-structured ETL/ELT pipelines, replacing ad-hoc scripts with orchestrated flows that can be maintained and monitored over time
• Conduct data audits that surface hidden revenue opportunities, margin leakages, and process inefficiencies, translating findings into concrete recommendations rather than stopping at the observation
• Partner with Finance, Sales, and Business Development to align data models and KPIs with the company’s revenue goals, ensuring the metrics leadership relies on are grounded in accurate, well-governed data
• Support Power BI dashboards focused on profitability and performance, backed by structured dbt models that give leadership real visibility into the drivers of business outcomes
• Maintain the clean, well-documented model-layer data that Data Science and AI teams depend on, ensuring downstream ML pipelines have access to consistent inputs from dbt rather than raw or inconsistently transformed sources
• Identify opportunities to integrate AI/ML outputs back into the data warehouse and reporting layer, making model predictions accessible and interpretable to business users
• Progressively learn, deploy, and support automated pipeline workflows and distributed workloads utilizing Prefect, n8n, and PySpark notebooks as part of your structured onboarding ramp-up
• Work closely with Marketing, Finance, Operations, and Product to translate ambiguous business questions into precise data models, pushing back constructively when the question isn’t yet precise enough to answer well
• Lead ongoing improvements to data workflows, reporting, and documentation so that institutional knowledge lives natively in code and version control rather than in individual contributors
• Communicate clearly with business stakeholders, ensuring data initiatives remain aligned with company strategy and that the people relying on the data understand what it’s actually telling them

Skills

• Expert-level dbt proficiency across advanced model design, testing frameworks (schema and singular tests), macros, packages, and documentation in a production-grade cloud environment
• Advanced SQL skills including window functions, CTEs, recursive queries, query plan analysis, and performance tuning, with direct experience handling cloud analytics database endpoints
• 4+ years of hands-on experience building and optimizing ETL/ELT pipelines within cloud infrastructure, with a strong preference for Azure environments
• Solid Python proficiency for foundational data engineering tasks, including pipeline scripting, API integrations, data validation, and process automation
• Hands-on exposure to cloud data warehouses (such as Azure Synapse Analytics, Snowflake, or Google BigQuery), with a strong willingness to master Microsoft Fabric architecture
• Familiarity with Power BI for reporting and dashboard delivery, particularly when backed by a structured, centralized semantic model layer
• Working knowledge of data modeling principles (Kimball, Data Vault, or similar), and practical experience applying them in a modern lakehouse or warehouse configuration
• Strong cross-functional communication skills, with the ability to explain complex technical engineering concepts without jargon and drive initiatives independently when needed
• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience demonstrating the same depth
• Production experience building and monitoring automated data pipelines with Prefect (including flows, tasks, and deployments) or equivalent orchestrators like Apache Airflow or Dagster
• Practical n8n experience building multi-step automation workflows that connect external APIs, databases, and internal business applications
• Experience or training with PySpark notebooks for distributed ETL, including DataFrame transformations, data cleansing, and partitioning strategies against lakehouse storage systems
• Experience with the dbt Semantic Layer or MetricFlow for centralized metric definitions shared across downstream visualization tools
• Microsoft Fabric exposure beyond SQL endpoints, including Eventstream, Real-Time Analytics, or OneLake shortcuts
• Background supporting Data Science or AI/ML teams as a reliable data platform partner, understanding what clean model-layer data means for downstream ML pipelines

Benefits

• Remote (U.S.); Monday - Friday
• 6:00 am to 3:00 pm PT OR 7:00 am to 4:00 pm PT

Company Overview