We are seeking for Data Engineers, Lead Data Engineers & Architects.
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Current CTC:
Expected CTC:
Notice Period(We prefer Immediate Notice)
Mode of Work: Remote/Hybrid
Employment Type:Full-Time / Contract
Job Location:Chennai/Bangalore
I.Data Engineers:
Responsibilities:
Build and maintain scalable, automated data ingestion and processing pipelines using Azure, dbt, Snowflake to extract data from various source systems and load it into Snowflake for further processing.
- Data Transformation & Modeling: Use dbt (data build tool) to develop modular, testable SQL transformations. Implement physical Data Vault 2.0 structures (Hubs, Links, Satellites) based on architectural designs.
- AI-Assisted Engineering: Actively use AI coding assistants (e.g., GitHub Copilot, ChatGPT) to generate boilerplate code, write dbt tests, draft documentation, and troubleshoot SQL/Python scripts, significantly accelerating your delivery cycle.
- Quality & Testing: Ensure high data quality by writing comprehensive dbt tests and implementing automated data validation checks within the E2E architecture.
- Collaboration: Work closely with the Tech Lead, Data Architect, and business analysts in an Agile environment to understand requirements and deliver reliable data sets for downstream reporting.
- Tools/Technologies: Snowflake, dbt, Azure (Data Factory, ADLS), SQL, Python, Git, CI/CD tools, AI-assisted coding tools.
- Experience: 3-6 years of hands-on experience in Data Engineering or Database Development.
- Technical Expertise: Strong proficiency in writing complex, optimized SQL and solid scripting skills in Python.
- Hands-on experience building pipelines in Microsoft Azure (ADF, Data Lake Storage).
- Practical experience developing and performance-tuning within Snowflake.
- Experience using dbt for data transformation, testing, and documentation.
- Familiarity with incorporating AI-assisted development tools (like GitHub Copilot) into daily coding workflows.
- Understanding of Data Vault 2.0 concepts (Hubs, Links, Satellites) and how to populate them.
II.Lead Data Engineer:
Responsibilities:
Serve as the hands-on technical expert for the engineering squad. Write complex dbt models, configure Azure Data Factory pipelines, and optimize Snowflake compute resources.
- Data Solutions Implementation: Translate logical Data Vault 2.0 models (provided by the Data Architect) into physical tables and automated data pipelines. Build the Hubs, Links, and Satellites with precision.
- AI-Driven Development: Lead by example in using AI-assisted coding tools (e.g., GitHub Copilot) to generate boilerplate SQL/dbt code, write documentation, and accelerate unit testing. Train the team to use these tools effectively and securely.
- Code Quality & Governance: Own the CI/CD pipeline and code review process. Enforce strict coding standards, version control (Git), and data validation testing within dbt.
- Agile Delivery: Partner with Product Owners and Scrum Masters to break down complex architectural epics into manageable sprint tasks. Unblock engineers and ensure sprint commitments are met.
- Tools/Technologies: Snowflake, dbt, Azure (Data Factory, Synapse, ADLS), Python, Git, CI/CD tools (Azure DevOps/GitHub Actions), AI-coding assistants.
- Mentorship: Conduct pair programming sessions, provide constructive feedback on pull requests, and upskill junior team members on modern cloud data warehousing and Data Vault concepts.
Requirements:
- Experience: 7-10 years of hands-on experience in Data Engineering, with at least 2+ years acting as a Tech Lead or Senior Engineer mentoring others.
- Technical Expertise: Expert-level SQL and strong programming skills in Python.
- Deep, hands-on experience building E2E data pipelines using Azure (ADF, Data Lake) and Snowflake.
- Extensive experience with dbt for building modular data transformations and testing.
- Practical knowledge of implementing Data Vault 2.0 physical structures.
- Proven experience utilizing AI-assisted development tools to improve coding efficiency.
- Strong background in Git, CI/CD, and Agile methodologies.
III.Data Architect
Responsibilities:
Design and own the conceptual, logical, and physical data models, specifically implementing Data Vault 2.0 architecture (Hubs, Links, Satellites).
- AI-Driven Efficiency: Integrate AI-assisted development tools (e.g., GitHub Copilot) into the data modeling and engineering workflows to automate repetitive SQL/DDL generation and speed up delivery.
- Strategic Influence: Define the overarching data architecture strategy, establishing patterns for historical data retention, auditability, and integration across the global enterprise.
- Stakeholder Collaboration: Act as the bridge between business domains and technical teams, translating complex business ontologies into robust Data Vault structures.
- Governance & Standards: Establish strict data modeling standards, hashing rules, and data lineage tracking while ensuring the architecture supports compliance and data privacy.
- Tools/Technologies: Data Vault 2.0 methodology, AI-coding assistants, data modeling tools (Erwin, Hackolade), Snowflake/Azure, and dbt.
Requirements:
- 10+ years of overall IT/Data experience, with at least 5+ years dedicated to Enterprise Data Architecture and Data Modeling.
- Deep, proven expertise in designing and implementing Data Vault 2.0 architectures (Hubs, Links, Satellites, Hash Keys, PIT/Bridge tables).
- Practical experience implementing and guiding teams on AI-assisted development tools (GenAI, LLMs, Copilot) to accelerate data engineering and modeling workflows.
- Advanced SQL proficiency and hands-on experience with modern cloud data platforms (e.g., Snowflake, Azure Synapse).
IV.Data Solution Architect (10-15 Years)
Responsibilities:
Take full ownership of the E2E analytics architecture, ensuring robust data pipelines from ingestion (Azure) to transformation (dbt) and consumption (Snowflake).
- Strategic Influence: Act as a technical authority, influencing global data strategy and guiding engineering teams on best practices for cloud data warehousing and data modeling.
- Stakeholder Collaboration: Partner closely with global business units, Product Owners, and Data Engineers to translate business requirements into technical blueprints.
- Innovation & Efficiency: Drive the adoption of AI-assisted development tools (e.g., GitHub Copilot) within the delivery teams to improve code quality and reduce time-to-market.
- Standards & Governance: Establish and enforce data governance, security, and performance standards across the analytics ecosystem, bringing MPG Standards to life through disciplined execution.
- Tools/Technologies: Snowflake, dbt, Azure (Data Factory, ADLS, etc.), Git, CI/CD pipelines, AI-coding assistants.
Requirements:
- Experience: 10+ years of overall experience in Data & Analytics, with at least 3+ years operating specifically as a Data/Solution Architect.
- Technical Expertise: Deep, hands-on architectural experience with Snowflake (data sharing, performance tuning, architecture).
- Expertise in dbt (data build tool) for data transformation and modeling.
- Strong background in the Microsoft Azure ecosystem (e.g., Azure Data Factory, Azure Data Lake Storage, Synapse).
- Proven track record of designing End-to-End (E2E) analytics architectures from source to visualization.
- Practical experience implementing and guiding teams on AI-assisted development tools to accelerate engineering workflows.
