Principal Data Infrastructure Developer

Role Overview: As a Senior Data Engineer at Accordion, your responsibility is to design and develop robust, scalable data platforms that power analytics and AI-driven decision-making across client organizations. You will need hands-on expertise in cloud-native data architecture, modern warehousing, and pipeline development. Additionally, you should be able to independently lead workstreams and mentor junior engineers. Taking an AI-first approach to every engagement is crucial, leveraging AI development tools to accelerate delivery and contributing to MCP server builds that expose data capabilities to AI agents and LLM-powered workflows. Key Responsibilities: - Partner with client stakeholders to understand business requirements and contribute to designing cloud-native data platform architectures that meet current and future needs. - Implement modern data integration patterns such as ELT/ETL orchestration, streaming pipelines, and real-time data processing. - Implement data solutions on technology-agnostic, cloud modern data platforms like Microsoft Fabric, Snowflake, Databricks, BigQuery, and Redshift. - Build data infrastructure that is AI-ready by design, ensuring proper governance, lineage, and accessibility patterns for downstream AI and analytics consumption. - Contribute to the build and deployment of MCP servers to expose data platform capabilities as tools consumable by AI agents and LLM-powered workflows. - Integrate/leverage AI tools responsibly to accelerate pipeline development, code reviews, and documentation. - Collaborate with multiple stakeholders to define KPIs, data models, and metrics frameworks aligned with client goals. - Partner with Project Managers to develop and execute project plans within agreed schedules and timelines. - Conduct knowledge transfer sessions and mentor junior engineers. - Stay current on emerging cloud platforms, data tools, and AI ecosystem developments. Qualifications Required: - An undergraduate degree (B.E/B.Tech.) from a tier-1 or tier-2 institution. - 36 years of experience in data engineering, data warehousing, or cloud data platform development. - Strong SQL proficiency, including query optimization, complex transformations, and data modeling across relational and columnar databases. - Hands-on experience with at least one of: Microsoft Fabric, Azure Synapse Analytics, Databricks, Snowflake, Google BigQuery, or AWS Redshift. - Experience with cloud-native orchestration tools like Azure Data Factory, Databricks Workflows, dbt, or Apache Airflow. - Familiarity with data lake and lakehouse architecture using open table formats (Delta Lake, Apache Iceberg) on cloud storage (ADLS, S3, GCS). - Experience with streaming platforms (Apache Kafka, Azure Event Hubs, AWS Kinesis) is a strong plus. - Hands-on experience owning end-to-end DevOps practices, including designing and implementing CI/CD pipelines for robust and scalable deployment of data solutions. - Working knowledge of BI tools such as Power BI, Tableau, Looker is a strong plus. - Awareness of AI-ready data design principles, governance, and accessibility for AI consumption. - Strong habit of responsibly using AI tools to improve delivery speed and code quality. - Proven ability to take initiative, drive innovation, and communicate effectively with stakeholders. Role Overview: As a Senior Data Engineer at Accordion, your responsibility is to design and develop robust, scalable data platforms that power analytics and AI-driven decision-making across client organizations. You will need hands-on expertise in cloud-native data architecture, modern warehousing, and pipeline development. Additionally, you should be able to independently lead workstreams and mentor junior engineers. Taking an AI-first approach to every engagement is crucial, leveraging AI development tools to accelerate delivery and contributing to MCP server builds that expose data capabilities to AI agents and LLM-powered workflows. Key Responsibilities: - Partner with client stakeholders to understand business requirements and contribute to designing cloud-native data platform architectures that meet current and future needs. - Implement modern data integration patterns such as ELT/ETL orchestration, streaming pipelines, and real-time data processing. - Implement data solutions on technology-agnostic, cloud modern data platforms like Microsoft Fabric, Snowflake, Databricks, BigQuery, and Redshift. - Build data infrastructure that is AI-ready by design, ensuring proper governance, lineage, and accessibility patterns for downstream AI and analytics consumption. - Contribute to the build and deployment of MCP servers to expose data platform capabilities as tools consumable by AI agents and LLM-powered workflows. - Integrate/leverage AI tools responsibly to accelerate pipeline development, code reviews, and documentation. - Collaborate with multiple stakeholders to define KPIs, data models, and metrics frameworks aligned with client goals. -