Lead Data Engineer
First seen by Alion on Sep 10, 2026.
Role Overview :
We are looking for a highly skilled Lead Data Engineer with 8 - 15 years of experience in designing, developing, and architecting scalable data engineering solutions.
The ideal candidate should have strong hands-on expertise in Advanced Python, Databricks, Unity Catalog, Delta Live Tables (DLT), dbt, Medallion Architecture, data pipelines, SQL, and cloud data platforms.
This is a hands-on technical leadership role involving solution design, architecture, development, code reviews, performance optimization, technical decision-making, and mentoring.
Tech Stack :
- Advanced Python, Databricks, Unity Catalog, Delta Live Tables (DLT), dbt, Medallion Architecture, SQL, Cloud Data Platforms.
Key Responsibilities :
Architecture & Solution Design :
- Design scalable, secure, and high-performance data engineering solutions using Databricks and modern cloud technologies.
- Translate business requirements into end-to-end data architecture and technical solutions.
- Design and implement Medallion Architecture using Bronze, Silver, and Gold layers.
- Define data ingestion, transformation, storage, governance, and consumption strategies.
- Create architecture diagrams, data flow diagrams, and technical documentation.
- Drive technical decisions related to scalability, performance, reliability, cost, and maintainability.
Advanced Python Development :
- Develop production-grade data engineering applications using Advanced Python.
- Build reusable Python frameworks, libraries, utilities, and data processing components.
- Implement complex data transformations, validations, exception handling, logging, and monitoring.
- Apply strong Python concepts including OOP, modular programming, design patterns, testing, packaging, and performance optimization.
- Develop scalable Python-based ETL/ELT pipelines and automation frameworks.
- Conduct code reviews and establish Python coding standards and best practices.
Databricks Engineering :
- Design and develop scalable data pipelines using Databricks Products, Python/PySpark, SQL, and Delta Lake.
- Work with Databricks Workflows, notebooks, compute, and job scheduling.
- Develop batch and near-real-time data processing pipelines.
- Optimize Spark jobs, SQL queries, cluster configurations, partitions, and data processing workloads.
- Implement Delta Lake capabilities including schema enforcement, schema evolution, partitioning, and optimization.
Unity Catalog :
- Implement Unity Catalog for centralized data governance and access management.
- Design catalogs, schemas, tables, views, external locations, and access controls.
- Implement role-based and fine-grained data access.
- Support data lineage, auditing, discovery, and governance requirements.
DLT / Lakeflow :
- Design and develop reliable pipelines using Delta Live Tables (DLT) and modern Databricks declarative pipeline capabilities.
- Develop batch and streaming data pipelines with appropriate data quality checks.
- Implement data quality expectations and validation rules.
- Monitor pipeline health, failures, performance, and data quality.
dbt :
- Develop dbt-based transformation frameworks integrated with Databricks.
- Create reusable models, macros, tests, sources, and documentation.
- Implement incremental models and dependency management.
- Establish data quality and testing standards using dbt.
Skills
Data Engineering, Python, Azure Databricks, SQL, Cloud, Data Management, Data Catalogue, Medallion Architecture, Data Pipeline