Sr. SQL Developer - Data Engineer
Senior SQL Developer / Data Engineer on a Data & AI team, building and optimizing complex SQL transformations and SQL-based ELT pipelines in a modern data lakehouse (Dremio, Databricks, or Snowflake). Focuses on data quality checks, pipeline monitoring, and data contracts with stakeholders.
- Compensation
- Not disclosed
- Location
- Hyderabad; Chennai
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- 6-10 years of experience
- B.E./B.Tech./M.E./M.Tech./MCA qualification
- Advanced SQL: complex SQL development, query optimization, debugging, performance tuning
- SQL as primary language for data transformations in production environments
- Hands-on experience with data lakehouse platforms (Dremio, Databricks, or Snowflake)
- Understanding of data products, data contracts, data modeling, and analytical data structures
- Experience building, maintaining, and optimizing SQL-based ELT pipelines
- Experience with data quality controls, validation checks, and pipeline monitoring
- Understanding of extracting/integrating data from databases, APIs, and other source systems
- Strong analytical and problem-solving skills; ability to independently own complex SQL transformations
Nice to have
- Experience with Informatica Intelligent Cloud Services (IICS)
- Hands-on experience with dbt (Data Build Tool)
- Familiarity with data governance, data lineage, and enterprise data management
Full description
Job Summary
We are looking for a highly skilled, SQL-focused Data Engineer / SQL Developer to join our Data & AI team. The ideal candidate will have strong hands-on expertise in advanced SQL development, query optimization, performance tuning, and SQL-based data transformations within production environments.
The role involves developing and maintaining reliable, high-quality data products in a modern data lakehouse architecture. The candidate will work closely with source systems, data owners, data product managers, and analytics teams to implement data contracts, maintain data quality, monitor pipelines, and ensure consistent and reliable data availability.
Key Responsibilities
- Develop, optimize, and maintain complex SQL transformations, ensuring high performance, accuracy, and maintainability.
- Build and manage SQL-based ELT pipelines within a modern data lakehouse environment.
- Implement data transformations based on defined data contracts and business requirements.
- Integrate source systems and extract data using established ingestion tools and frameworks.
- Monitor data pipelines, identify issues, troubleshoot failures, and ensure timely data availability.
- Implement changes requested by data owners and stakeholders, translating requirements into structured SQL logic.
- Establish and maintain data quality rules, validation checks, and technical controls within SQL and pipeline workflows.
- Troubleshoot SQL queries, data inconsistencies, performance bottlenecks, and pipeline failures.
- Collaborate with data product managers and analytics teams to ensure data products comply with agreed data contracts.
- Follow SQL coding standards, performance optimization practices, and data governance guidelines.
- Document SQL transformations, data contracts, validation rules, and technical processes.
Required Technical Skills
- Advanced SQL Expertise (Mandatory): Strong proficiency in complex SQL development, query optimization, debugging, and performance tuning.
- SQL-Based Data Transformation: Proven experience using SQL as the primary language for data transformations in production environments.
- Data Lakehouse Technologies: Hands-on experience with modern data lakehouse platforms such as Dremio, Databricks, or Snowflake.
- Data Products & Contracts: Strong understanding of data products, data contracts, data modeling, and analytical data structures.
- ELT Pipelines: Experience building, maintaining, and optimizing SQL-based data transformation pipelines.
- Data Quality & Monitoring: Experience implementing data quality controls, validation checks, technical checks, and pipeline monitoring.
- Source System Integration: Understanding of extracting and integrating data from databases, APIs, and other source systems.