Role: Lead Data Engineer

Location: Pune Hinjewadi Phase 2

Mode of Work: Hybrid

Total exp: 8 Years

Client: Inferyx- Full-time

Interview Mode: 2 Virtual and 1 F2F

Certification: Databricks (Mandatory)

Key Responsibilities:

Data Pipeline Development

Design, develop, and maintain scalable batch and streaming data pipelines using Apache Spark (PySpark/Scala) and Databricks. Build end-to-end ETL/ELT workflows for ingesting, transforming, and validating data from diverse source systems while ensuring data accuracy, reliability, and performance.

Data Modeling & Analytics Enablement

Design and maintain efficient data models, schemas, and curated datasets that support business analytics, reporting, and visualization tools. Optimize data structures for performance, scalability, and cost across lakehouse and data warehouse platforms.

Data Integration

Integrate data from multiple internal and external sources, including relational databases, APIs, flat files, and streaming sources. Ensure seamless and reliable data movement across cloud platforms, data lakes, and analytics systems.

Performance Optimization

Identify and resolve performance bottlenecks in Spark jobs, Databricks workloads, and data storage layers. Tune Spark configurations, optimize queries, and improve pipeline efficiency to support large-scale data processing.

Data Quality & Governance

Implement data quality checks, validation rules, and governance standards to ensure trustworthy data. Monitor data quality metrics and proactively address data issues in collaboration with stakeholders.

Collaboration & Stakeholder Engagement

Work closely with data analysts,



data scientists, and business teams to understand requirements and deliver data solutions aligned with business objectives. Partner with platform and cloud teams to ensure architectural consistency and best practices.

Documentation & Best Practices

Document data pipelines, data models, and technical designs. Follow best practices for software development, version control, CI/CD, and deployment in distributed data environments.

Continuous Improvement

Stay current with emerging data engineering technologies, Spark and Databricks enhancements, and cloud data platform innovations. Drive automation and process improvements to increase reliability, scalability, and developer productivity.

Required Skills and Qualifications:

- Bachelor's degree in Computer Science, Engineering, or related field.
- 8 years of experience in data engineering or related roles.
- Proficiency in programming languages such as Python, Java, or Scala.
- Strong SQL skills and experience with relational databases (e.g., MySQL, PostgreSQL).
- Experience with data warehousing concepts and technologies (e.g., Snowflake, Redshift).
- Familiarity with big data processing frameworks (e.g., Apache Spark, Hadoop).
- Hands-on experience with ETL tools and data integration platforms.
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Understanding of data modeling principles and data warehousing design patterns.
- Excellent problem-solving skills and attention to detail.
- Solid communication and collaboration skills, with the ability to work effectively in a team environment.

📌 Data Engineer (Pune)
🏢 NAM Info
📍 Pune