Sr. Analytics Engineer - Remote India

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.

Hours of Expectation

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. Product Analytics & Data Products

• Partner directly with Engineering and Product teams to design and ship customer- and internal-facing data products—embedded analytics, usage and product-instrumentation models, and event/telemetry pipelines—treating downstream engineering consumers as first-class stakeholders.
• Own product analytics modeling: define and instrument the metrics behind product engagement, adoption, funnels, and feature usage, and translate product questions into trustworthy, reusable datasets.
• Support engineering workflows by exposing well-contracted, API- and application-ready data models, and by collaborating on the data layer that powers in-product features and services.
• Act as the connective tissue between analytics and software delivery—participating in product/engineering planning, understanding roadmaps, and ensuring data is designed into products from the start rather than bolted on.

6. 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

• Automotive / Fixed Ops Domain (Highly Desirable): Prior experience with automotive repair order data or dealership fixed operations (service, parts, and repair-order lifecycle) is highly desirable.
• 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.
• Product & Engineering Partnership: Proven experience partnering with Product and Engineering teams to build data products, instrument product analytics, and support engineering-facing data needs—knowing how to enable, not just report to, the teams building the software.
• Core Languages: Very strong, expert-level SQL and Python skills for transformation, automation, and tooling.
• Transformation: Deep hands-on experience with dbt (Core or Cloud) building modular, tested, version-controlled transformation pipelines.
• Platforms: Deep hands-on experience with Snowflake or Databricks, ideally within an AWS ecosystem.
• BI & Semantics: Proven track record delivering governed metrics and curated datasets to BI tools such as Tableau, Power BI, or Looker.
• Domain Analytics Experience (Desirable): Bonus if you have hands-on experience building and maintaining analytics model libraries across core business domains—such as Finance (revenue, margin, cost, budget vs. actuals), Sales (pipeline, bookings, attainment), and Order/Operations (order-to-cash, fulfillment, returns)—translating domain requirements into reusable, well-documented models.
• Data Governance & Tooling (Desirable): Experience working with data governance tools and platforms such as KNIME for data quality, profiling, cataloging, and governed analytics workflows.
• Data Validation: Demonstrated experience implementing automated testing frameworks, data profiling, and pipeline validation (owning the QA of your own models).
• Soft Skills: Strong documentation habits (playbooks, technical specs, data dictionaries) and an ownership mindset.
• Certifications (Nice-to-Have): Relevant IT professional certifications, such as dbt Analytics Engineering Certification, SnowPro Core, Databricks Certified Data Engineer Professional, or AWS Certified Data Engineer.

• Opportunity to build and scale the data foundation of a growing, AI-enabled SaaS company.
• High-impact role supporting real-time analytics, machine learning, enterprise reporting, and product innovation.
• Close partnership across Data, Product, Engineering, Analytics, and business leadership.
• Values-driven culture built on accountability, urgency, and delivering measurable results.
• Remote-first environment offering flexibility, autonomy, and trust.