Sr. Analytics Engineer - Remote India
Sr. Analytics Engineer
Position Overview
Dynatron is seeking a highly-skilled Senior Analytics Engineer to join our growing data team. Where our data engineers build the pipelines that move and land raw data, you will be the lead craftsman responsible for transforming that data into clean, reliable, and well-documented models that power our self-service analytics, executive dashboards, and decision-making across the business. You are a hands-on expert in dbt and modern cloud data warehouses, specifically Snowflake or Databricks, and you bring the software engineering rigor needed to treat analytics code as a production-grade product.
Work Hours & Collaboration Expectations
Critical hours to be available for collaboration with the US team are:
- 9:00 AM – 2:00 PM EST
- 8:00 AM – 1:00 PM CST
- 7:00 AM – 12:00 PM MST
- 6:00 AM – 11:00 AM PST
The balance of 3 hours each day can be worked before or after core hours at your discretion.
This role includes on-call responsibilities; the engineer is expected to participate in a rotation to monitor pipeline health and respond to production data issues outside of core hours as needed.
Key Responsibilities
1. Data Modeling & Transformation
- Design, build, and maintain modular, well-tested transformation layers in dbt, following Medallion (bronze/silver/gold) and Dimensional Modeling best practices.
- Translate raw, source-conformed data into curated, analytics-ready marts that serve as a single source of truth for the business.
- Develop reusable macros, packages, and modeling standards that keep the warehouse consistent, performant, and easy to extend.
- Optimize warehouse compute and storage (clustering, materializations, incremental models) to ensure high-performance, cost-effective transformations.
2. Metrics, Semantics & BI Enablement
- Own the semantic and metrics layer, defining governed, version-controlled business metrics that produce consistent numbers across every report and dashboard.
- Partner with BI developers and analysts to expose trusted datasets through tools such as Tableau, Power BI, or Looker.
- Build and maintain documentation, data dictionaries, and lineage so stakeholders can discover and trust the data they consume.
- Build and maintain domain-specific analytics model libraries (e.g., Finance, Sales, and Order/Operations) that standardize how each domain's metrics and reporting are defined and consumed.
3. Data Quality & Automated Testing (QA Ownership)
- Own end-to-end data validation by building automated tests (dbt tests, custom assertions, anomaly checks) directly into the transformation workflow.
- Enforce data contracts and schema evolution guidelines to maintain high data quality and integrity across domains.
- Implement proactive alerting and observability to catch data drift, freshness failures, and quality drops before they reach downstream users.
4. Analytics Engineering for ML/AI
- Curate and maintain clean, feature-ready datasets that support the Data Science team and downstream ML workflows.
- Collaborate on operationalizing analytics within services such as Snowflake Cortex, Databricks AI, or AWS Bedrock.
5. Technical Leadership & Collaboration
- Mentor junior analysts and engineers in SQL optimization, dbt best practices, and analytics engineering workflows.
- Collaborate closely with Product, Engineering, and business stakeholders to translate analytical requirements into well-modeled, functional code.
Required Qualifications
- Experience: 6-8+ years of experience in analytics engineering, data analytics, or data engineering with a focus on data modeling and transformation.
- Lifecycle Ownership: Demonstrated experience owning the complete development lifecycle, from requirements and design through testing, deployment, and production launch.
- Core Languages: Very strong, expert-level SQL and Python skills for transformation, automation, and tooling.
- Transformation: Deep hands-on experience with dbt (Core