Lead Data Engineer – Analytics & AI

The Role

You’ll lead the detailed technical design and engineering delivery of enterprise data, analytics, and AI/ML solutions. Working closely with Solution Architects, you’ll turn high-level architecture into practical designs that engineering teams can build, operate, and scale.

This is a hands-on technical leadership role. You’ll have significant influence over data models, pipelines, engineering standards, and implementation quality while helping Data Engineering and AI/ML teams solve complex delivery and production challenges.

What You’ll Do

- Turn architecture into working solutions. You’ll translate high-level designs into detailed data models, mappings, transformations, interfaces, pipeline designs, and implementation specifications that developers can execute against.
- Own data engineering design quality. You’ll design scalable data warehouse, lakehouse, and curated data structures that support reporting, dashboards, analytics, KPIs, and AI/ML use cases.
- Lead complex data engineering delivery. You’ll guide developers through implementation, review designs and code, establish practical engineering standards, and help ensure solutions are maintainable and production-ready.
- Design for scale and reliability. You’ll make appropriate design choices across batch, incremental, CDC, and streaming workloads while considering performance, scalability, data quality, and operational support.
- Solve difficult technical problems. You’ll troubleshoot complex data, integration, and performance issues, including production and post-go-live problems where the root cause may span multiple systems.
- Connect data engineering with analytics and AI.



You’ll work with analytics and AI/ML teams to ensure data models and curated datasets support predictive analytics, forecasting, anomaly detection, visualization, and other advanced use cases.
- Raise the technical capability of the team. You’ll mentor engineers, provide technical direction, encourage sound engineering practices, and help teams adopt appropriate new technologies.

What You’ll Need

Must-haves

- Significant professional experience in data engineering, data warehousing, or data architecture, including experience leading the technical design or delivery of enterprise data solutions.
- Strong hands-on knowledge of SQL, data modelling, ETL/ELT, data pipelines, and enterprise data warehouse or lakehouse architectures.
- Experience translating solution architecture or high-level designs into detailed technical designs that engineering teams can implement.
- Ability to lead engineering delivery, review technical work, and diagnose complex integration, scalability, performance, and production issues.
- Strong communication and problem-solving skills, including the ability to work effectively with architects, developers, analytics teams, and other stakeholders.

Nice-to-haves

- Hands-on experience with technologies such as Python, Spark/PySpark, Databricks, Snowflake, Oracle ADW, Redshift, Azure Synapse, Kafka, Airflow, ADF, AWS Glue, or similar platforms.




- Experience with cloud platforms such as AWS, Azure, or OCI, along with Git and CI/CD practices.
- Exposure to AI/ML data engineering, MLOps, GenAI/LLM-enabled analytics, data governance, lineage, quality, or observability.
- Experience with the utilities industry, particularly metering, customer, outage, grid, or asset data; Oracle Utilities C2M/NMS exposure is an additional advantage.

What We Offer

You’ll have the opportunity to take technical ownership of complex enterprise data initiatives, influence engineering standards, and grow further into senior technical architecture or engineering leadership roles.

You’ll work across up-to-date data, analytics, and AI technologies rather than being limited to a single platform or toolset.

The Team

You’ll work closely with Solution Architects and collaborate with Data Engineering, Analytics, and AI/ML teams. The role sits between architecture and implementation: you’ll be expected to understand the broader solution while remaining close enough to the engineering work to make practical technical decisions.

The role can involve technically complex environments, unfamiliar technologies, and production issues that require structured troubleshooting rather than straightforward implementation.

How to Apply

Please submit your resume along with a short note describing one enterprise data platform, warehouse, or lakehouse solution that you personally helped design or technically lead. Briefly explain your role, the scale or complexity of the solution, and the key technical decisions you owned.

Qualified candidates are encouraged to apply even if they have not worked with every technology listed above.

📌 Lead Data Engineer – Analytics & AI (Hyderabad)
🏢 Abjayon
📍 Hyderabad