1. Data Engineering Accelerator R&D;

- Understand existing accelerators, validate their capabilities and outputs, recommend improvements, and deliver client demos.

- Work with the founder to prioritize new accelerator capabilities across the data engineering lifecycle.

- Translate data engineering challenges into clear requirements, workflows, and acceptance criteria for AI engineers.

- Provide domain guidance on architecture, database development, data modeling, pipelines, and modernization.

- Define accelerator inputs, expected outputs, engineering rules, and validation checks.

- Create realistic test scenarios and review generated designs, models, and code for correctness, completeness, and performance.

- Work hands-on with AI engineers to resolve issues and improve output quality.

- Capture client feedback and document reusable patterns, standards, and lessons learned.

1. Technical Leadership for Client Data Programs

- Support client proposals with solution approaches, scope, effort estimates, and delivery plans.

- Guide engineering teams and review architectures, designs, and code.

- Attend client calls to clarify requirements, explain recommendations, and resolve technical concerns.

- Contribute hands-on to advisory, solution design, development, and troubleshooting as needed.

- Manage technical risks and dependencies and help teams resolve delivery blockers.

- Apply relevant accelerators to client programs and use delivery feedback to improve them.

Required Skills and Experience

- Strong database fundamentals across OLTP, OLAP, NoSQL, data warehouses, lakehouses, and analytical systems.

- Proven hands-on database development experience, including advanced SQL, stored procedures, indexing, and performance tuning.

- End-to-end delivery experience in both greenfield data platforms and brownfield modernization,



from requirements and architecture through deployment and operations.

- Strong Python skills for data processing, integration, and automation.

- Ability to translate business requirements into practical designs and explain architecture trade-offs.

- Strong technical leadership, client communication, problem-solving, and ownership.

- Ability to guide AI engineers and critically validate AI-generated technical outputs.

Required Technical Coverage – Full Data Engineering Stack

Hands-on experience across a complete data engineering stack is mandatory, from source integration and storage through transformation, modeling, reporting, and operations. Candidates must have delivered solutions on at least one up-to-date data platform.

Experience with every listed tool is not required; equivalent technologies are acceptable.

- Data platforms: Microsoft Fabric, Databricks, Snowflake, Amazon Redshift, or Google BigQuery.

- Databases: SQL Server, Oracle, PostgreSQL, MySQL, or equivalent, with an understanding of NoSQL use cases.

- Pipelines and orchestration: Azure Data Factory (ADF), Fabric Data Factory, Airflow, or equivalent.

- Data processing: SQL, Python, and Spark/PySpark or equivalent distributed processing tools.

- ETL/ELT design: Source-to-target mapping, transformations, incremental loading, CDC, error handling, and reconciliation.

- Data modeling: Conceptual, logical, physical, and dimensional models, including star schemas and slowly changing dimensions.

- Reporting and analytics: Power BI or equivalent, semantic models, business metrics, and analytics-ready datasets.

- Quality and governance: Profiling, validation, metadata, lineage, access controls, and sensitive-data handling.

- Engineering and operations: Git, CI/CD, automated testing, monitoring, and performance and cost optimization.

📌 Lead Data Engineer – Architecture (Tamil Nadu)
🏢 AES Technologies India Pvt
📍 Tamil Nadu