Sr. Analytics Engineer
New
India, Flexible working schedule designed to support collaboration with international teamsFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6–8+ years
- Required Skills
- AWSPythonSQLMicrosoft Power BISnowflakeTableauData modelingdbtDatabricksLooker
Requirements
- 6–8+ years of experience in analytics engineering, data analytics, or data engineering, with strong expertise in data modeling and transformation.
- Proven experience owning the full development lifecycle, including requirements gathering, solution design, testing, deployment, and production support.
- Expert-level proficiency in SQL and Python for data transformation, automation, and analytics engineering workflows.
- Extensive hands-on experience with dbt Core or dbt Cloud, including building modular, tested, and version-controlled transformation pipelines.
- Strong experience with modern cloud data platforms such as Snowflake or Databricks, ideally within an AWS-based environment.
- Experience designing governed metrics, semantic layers, and curated datasets for business intelligence platforms including Tableau, Power BI, or Looker.
- Strong understanding of automated testing frameworks, data validation practices, and analytics pipeline monitoring.
- Excellent documentation skills, with experience creating technical specifications, playbooks, and data dictionaries.
Responsibilities
- Design, build, and maintain scalable, modular, and well-tested data transformation models using dbt, following modern data modeling principles such as Medallion architecture and dimensional modeling.
- Transform raw data into trusted analytics-ready datasets and data marts that support business reporting, executive dashboards, and self-service analytics.
- Develop reusable modeling frameworks, macros, packages, and standards to improve consistency, maintainability, and performance across the data warehouse.
- Optimize cloud data warehouse performance and cost through effective use of clustering, materializations, incremental models, and other optimization techniques.
- Own the semantic and metrics layer by defining governed, version-controlled business metrics that ensure consistency across reporting and analytics platforms.
- Partner with BI teams and analysts to deliver reliable datasets through tools such as Tableau, Power BI, and Looker.
- Build automated data quality checks, validation frameworks, anomaly detection processes, and monitoring solutions to maintain data accuracy and reliability.
- Mentor junior team members and promote best practices in SQL optimization, dbt development, analytics engineering workflows, and data documentation.
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