Analytics Engineer

Job Description

Job Description :

We are seeking a proactive and skilled Analytics Engineer to join our Data team. This mid-level role is crucial for bridging the gap between raw data and actionable business insights. You will be responsible for designing, developing, and maintaining scalable and reliable data models that empower our Analysts, Data Scientists, and business stakeholders to make data-driven decisions. The ideal candidate has a strong background in data warehousing, advanced SQL, and modern data transformation tools.

Key Responsibilities :

Data Modelling & Transformation (ELT) :

Design, develop, and implement production-ready data models (e.g., dimensional modelling) within the cloud data warehouse.

Develop, test, and maintain robust data transformation logic using dbt (data build tool) and SQL to clean, enrich, and standardise raw data.

Ensure data definitions, metrics, and business logic are consistent and accurately applied across all data models.

Business Dashboards :

Design and optimise reporting tables / marts specifically to support core business dashboards in tools like Looker or Power BI.

Ensure that the underlying data models are highly performant for BI tool queries (e.g., proper aggregation, indexing, and partitioning to minimise query run time).

Develop semantic layer definitions (e.g., LookML in Looker, calculated fields in Power BI) to define and standardise key business metrics for self-service analysis.

Business Intelligence :

Serve as a key link between business stakeholders and the data platform, translating vague business questions into measurable metrics, clear analytical requirements, and data model designs.

Conduct exploratory data analysis on structured data sets to uncover trends, root causes for business performance changes, and new insights that drive strategic decisions.

Provide training and support to internal business users (e.g., Marketing, Finance, Product teams) on how to effectively use the standardised BI tools and data models for their own self-service analysis.

Data Quality & Governance :

Implement automated data quality checks and monitoring within the ELT process to ensure data integrity and reliability.

Maintain clear and comprehensive documentation for data models, business logic, and lineage.

Collaborate with Data Governance teams to ensure compliance and security standards are met.

Pipeline Support :

Work closely with Data Engineers to ensure efficient data ingestion and flow into the data warehouse.

Requirements

Requirements :

B.E. / B.Tech. / M.E. / M.Tech. / MCA / M.Sc.IT or related field

4–5 years of professional experience in an Analytics Engineering, Data Engineering, or a highly relevant Data Analyst role.

Expert-level proficiency in SQL for complex querying, window functions, and performance optimisation.

Strong practical experience with a modern Cloud Data Warehouse (e.g., Snowflake, Google BigQuery, AWS Redshift).

Demonstrated experience with a data transformation tool, such as dbt (Data Build Tool), is highly preferred.

Proficiency in at least one scripting language (Python preferred) for automation and data manipulation.

Solid understanding of data modelling techniques

Familiarity with version control best practices (e.g., Git).

Strong analytical and problem-solving skills

Excellent communication and interpersonal skills

Nice to Have Skills

Experience with workflow orchestration tools (e.g., Airflow).

Familiarity with CI / CD principles as applied to data pipelines.

Experience with Agile methodologies

Benefits

• Service recognition awards
• Market-leading salary packages
• Maternity & Paternity Benefits
• Medical Insurance Benefits

Requirements

Data Modelling & Transformation (ELT) : Design, develop, and implement production-ready data models (e.g., dimensional modelling) within the cloud data warehouse. Develop, test, and maintain robust data transformation logic using dbt (data build tool) and SQL to clean, enrich, and standardise raw data. Ensure data definitions, metrics, and business logic are consistent and accurately applied across all data models. Business Dashboards : Design and optimise reporting tables / marts specifically to support core business dashboards in tools like Looker or Power BI. Ensure that the underlying data models are highly performant for BI tool queries (e.g., proper aggregation, indexing, and partitioning to minimise query run time). Develop semantic layer definitions (e.g., LookML in Looker, calculated fields in Power BI) to define and standardise key business metrics for self-service analysis. Business Intelligence : Serve as a key link between business stakeholders and the data platform, translating vague business questions into measurable metrics, clear analytical requirements, and data model designs. Conduct exploratory data analysis on structured data sets to uncover trends, root causes for business performance changes, and new insights that drive strategic decisions. Provide training and support to internal business users (e.g., Marketing, Finance, Product teams) on how to effectively use the standardised BI tools and data models for their own self-service analysis. Data Quality & Governance : Implement automated data quality checks and monitoring within the ELT process to ensure data integrity and reliability. Maintain clear and comprehensive documentation for data models, business logic, and lineage. Collaborate with Data Governance teams to ensure compliance and security standards are met. Pipeline Support : Work closely with Data Engineers to ensure efficient data ingestion and flow into the data warehouse.