Designation : Assistant Manager
Level : L3
Location : Chennai
Experince : 5 to 8 Years
Job Role :
proactive and detail-oriented Analytics Engineer to bridge the gap between raw data engineering and business analytics. In this role, your primary focus will be designing, building, and maintaining robust, scalable data pipelines and transformed datasets that empower our analytics and business teams to derive clear insights.
You will own the transformation layer of our data stack—turning raw, multi-source data into clean, well-modeled, and well-documented data products.
Responsibilities :
Data Pipeline & Transformation: Build, optimize, and maintain automated ELT/ETL data pipelines to ingest and transform data from diverse operational systems into our data warehouse.
Data Modeling: Design clean, scalable, and intuitive dimensional data models (e.g., Kimball methodology, star schemas) tailored for analytics and reporting performance.
Analytics Support: Collaborate closely with Data Analysts, Data Scientists, and Business Stakeholders to understand their data requirements and translate them into reliable data pipelines.
Data Quality & Testing: Implement automated testing, monitoring, and data validation rules to ensure high data integrity, reliability, and freshness.
Documentation & Governance: Maintain clear documentation of data lineage, data dictionaries, and transformation logic to maintain high data governance standards.
Performance Optimization: Audit and optimize slow-running SQL queries, data transformations, and data pipeline execution time
Required skills :
Advanced SQL: Expertise in writing complex, highly performant SQL queries, window functions, CTEs, and query optimization. Data Transformation & Modeling: Strong hands-on experience using modern transformation tools like dbt (data build tool) and a solid foundation in data warehousing methodologies (star schema, snowflake schema). Cloud Data Warehouses: Experience working with cloud data warehouses such as Snowflake, Google BigQuery, Amazon Redshift, or Databricks. Data Pipeline Orchestration: Proven experience building and orchestrating pipelines using tools like Apache Airflow, Dagster, Prefect, or Fivetran. Version Control & CI/CD: Proficiency with Git and modern software engineering practices applied to data (code reviews, testing, deployment pipelines).
Python / Scripting: Familiarity with Python for custom data scripting, API integrations, and automation (e.g., pandas, requests). BI & Visualization Integration: Experience interfacing with Business Intelligence tools (e.g., Looker, Tableau, Power BI) to optimize datasets for reporting performance. Data Governance & Quality: Hands-on experience with data quality and testing tools like Great Expectations or Soda.
