Simpliigence
Website:
simpliigence.com
Job details:
We are seeking for Cloud Data Engineers, Lead Cloud Data Engineers & Cloud Data Architects.
Please share an updated profile to
kavitha@simpliigence.com
(+91)74839 25904
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.Cloud Data Engineer:
Responsibilities:
Cloud Data Engineer with expertise 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
Ensure data quality and consistency by applying DBT tests and custom SQL validation rules before loading.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.
- 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.
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.
Click on Apply to know more.