Senior Analytics Engineer - ETL/Snowflake DB

Job Title : Senior Analytics EngineerLocation : Remote (India)Working Hours : Aligned with UK Business HoursRole Overview :We are hiring a Senior Analytics Engineer to own the reliability, scalability, governance, and evolution of our analytics platform. You will design and maintain production-grade dbt models, semantic data layers, and trusted data products while ensuring high standards for data quality, observability, and self-service analytics. Working closely with the Lead Data Analyst and UK stakeholders, you will translate complex business processes into scalable analytical models, maintain robust data pipelines, and deliver trusted reporting solutions for operations, product, and commercial teams. This role requires strong hands-on experience with dbt, Snowflake, SQL, ThoughtSpot, ETL pipelines, GitHub workflows, and modern analytics engineering best practices, along with prior experience working with e-commerce, subscription, or operational data.Key Responsibilities :Analytics Engineering & Data Modelling :- Design, build, and maintain production-grade dbt models using layered architecture (staging, intermediate, marts) following analytics engineering best practices.- Extend and optimize existing production dbt models while maintaining clean model lineage and governance standards.- Develop scalable semantic data models and reusable data products that serve as the single source of truth across the business.- Build analytics models across customer, orders, products, inventory, fulfilment, marketing, suppliers, forecasting, warehouse operations, and operational KPI domains.- Translate operational and commercial business processes into scalable analytical models rather than simply reporting raw transactional data.Data Quality, Reliability & Governance :- Own the reliability, observability, and governance of the analytics platform.- Implement robust data quality frameworks including automated testing, freshness SLAs, monitoring, alerting, schema documentation, exposures, and semantic modelling.- Develop robust data cleansing, validation, reconciliation, and transformation logic to ensure trusted analytical datasets.- Ensure every dbt pull request meets governance standards, including schema.yml documentation, testing, freshness SLAs, and in-code documentation.SQL & Data Warehousing :- Write highly optimized SQL in Snowflake using advanced concepts including window functions, QUALIFY, CTEs, role-based access patterns, and performance optimization.- Maintain and extend canonical data models across customer, order, product, marketing, and inventory domains.- Troubleshoot production SQL issues while ensuring scalable warehouse performance.ETL & Data Pipelines :- Build, manage, and troubleshoot ETL/ELT pipelines using Airbyte, Fivetran, Stitch, Hevo, or similar tools.- Resolve connector failures, schema drift, incremental loading challenges, late-arriving facts, duplicate handling, and recovery scenarios.- Understand dbt DAGs and orchestration dependencies, assessing upstream and downstream impact before implementing changes.BI & Self-Service Analytics :- Develop self-service dashboards, ThoughtSpot Liveboards, worksheets, semantic models, and reusable metrics for business users.- Manage ThoughtSpot search definitions, groups, permissions, and Spotter access.- Enable non-technical stakeholders to access trusted analytical insights through scalable semantic layers.Collaboration & Delivery :- Partner with Operations, Commercial, Finance, Product, and Business stakeholders to understand business processes and translate them into reusable analytical solutions.- Investigate root causes of operational issues and develop scalable reporting solutions.- Own analytics deliverables from requirements gathering through implementation, testing, validation, deployment, monitoring, and production support.- Communicate progress proactively through clear and concise asynchronous updates while collaborating effectively with UK stakeholders.Required Skills & Experience :Core Requirements :- 5+ years of experience in Analytics Engineering, Data Engineering, or similar data-focused roles.- Strong production experience with dbt.- Advanced SQL expertise on Snowflake.- Strong understanding of dimensional modelling, warehouse architecture, semantic layers, and analytics engineering best practices.- Experience implementing data quality frameworks with automated testing, monitoring, observability, alerting, and freshness SLAs.- Hands-on production experience with ETL/ELT platforms such as Airbyte, Fivetran, Stitch, Hevo, or custom-built ingestion pipelines.- Experience troubleshooting connector failures, schema drift, incremental loading, recovery patterns, and production data pipelines.- Strong understanding of dbt DAGs, orchestration dependencies, and upstream/downstream model impacts.- Experience working with GitHub workflows including branch-per-PR development, code reviews, version control, and clean commit practices.- Hands-on experience with ThoughtSpot including Liveboards, worksheets, semantic models, search definitions, user groups, and Spotter access management.- Strong understanding of operational, e-commerce, or subscription data.- Strong written and verbal English communication skills with experience collaborating across distributed teams.Preferred Skills :- Experience integrating PostHog event data into Snowflake and dbt models.- Experience working with PostHog funnels, events, feature flags, and experiment reporting.- Understanding of A/B testing methodologies including experiment design, statistical significance, lift analysis, and experiment dashboards.- Experience with Reverse ETL platforms such as Hightouch, Census, Klaviyo, or similar customer activation tools.- Experience working with multi-environment dbt deployments (Development, Production, PII-restricted environments) and job selectors.- Ability to model complex business workflows across ERP, WMS, e-commerce, supplier, finance, and operational systems.- Strong focus on data governance, documentation, semantic modelling, and enterprise data quality.Nice to Have :- Python experience using pandas, scikit-learn, or statsmodels for data validation, reconciliation, automation, or predictive modelling.- Experience building churn prediction, customer lifetime value (LTV), propensity models, or forecasting models.- Knowledge of model validation techniques including time-based validation, cross-validation, calibration, and lift analysis.- Experience with orchestration platforms such as dbt Cloud, Airflow, or similar workflow management tools.- Exposure to modern observability and monitoring frameworks within cloud data platforms. (ref:hirist.tech)