Job
Description
Job Title: Senior Data Engineer / Technical Lead Location: Bangalore Employment Type: Full-time Role Summary We are seeking a highly skilled and motivated Senior Data Engineer/Technical Lead to take ownership of the end-to-end delivery of a key project involving data lake transitions, data warehouse maintenance, and enhancement initiatives. The ideal candidate will bring strong technical leadership, excellent communication skills, and hands-on expertise with modern data engineering tools and platforms. Experience in Databricks and JIRA is highly desirable. Knowledge of supply chain and finance domains is a plus, or a willingness to quickly ramp up in these areas is expected. Key Responsibilities Delivery Management Lead and manage data lake transition initiatives under the Gold framework. Oversee delivery of enhancements and defect fixes related to the enterprise data warehouse. Technical Leadership Design and develop efficient, scalable data pipelines using Python, PySpark , and SQL . Ensure adherence to coding standards, performance benchmarks, and data quality goals. Conduct performance tuning and infrastructure optimization for data solutions. Provide code reviews, mentorship, and technical guidance to the engineering team. Collaboration & Stakeholder Engagement Collaborate with business stakeholders (particularly the Laboratory Products team) to gather, interpret, and refine requirements. Communicate technical solutions and project progress clearly to both technical and non-technical audiences. Tooling and Technology Use Leverage tools such as Databricks, Informatica, AWS Glue, Google DataProc , and Airflow for ETL and data integration. Use JIRA to manage project workflows, track defects, and report progress. Documentation and Best Practices Create and review documentation including architecture, design, testing, and deployment artifacts. Define and promote reusable templates, checklists, and best practices for data engineering tasks. Domain Adaptation Apply or gain knowledge in supply chain and finance domains to enhance project outcomes and align with business needs. Skills and Qualifications Technical Proficiency Strong hands-on experience in Python, PySpark , and SQL . Expertise with ETL tools such as Informatica, AWS Glue, Databricks , and Google Cloud DataProc . Deep understanding of data warehousing solutions (e.g., Snowflake, BigQuery, Delta Lake, Lakehouse architectures ). Familiarity with performance tuning, cost optimization, and data modeling best practices. Platform & Tools Proficient in working with cloud platforms like AWS, Azure, or Google Cloud . Experience in version control and configuration management practices. Working knowledge of JIRA and Agile methodologies. Certifications (Preferred but not required) Certifications in cloud technologies, ETL platforms, or relevant domain (e.g., AWS Data Engineer, Databricks Data Engineer, Supply Chain certification). Expected Outcomes Timely and high-quality delivery of data engineering solutions. Reduction in production defects and improved pipeline performance. Increased team efficiency through reuse of components and automation. Positive stakeholder feedback and high team engagement. Consistent adherence to SLAs, security policies, and compliance guidelines. Performance Metrics Adherence to project timelines and engineering standards Reduction in post-release defects and production issues Improvement in data pipeline efficiency and resource utilization Resolution time for pipeline failures and data issues Completion of required certifications and training Preferred Background Background or exposure to supply chain or finance domains Willingness to work during morning US East hours Ability to work independently and drive initiatives with minimal oversight Required Skills Databricks,Data Warehousing,ETL,SQL
