Senior Data Engineer
On-site · Pune, Maharashtra, India
Job Summary
Design and deliver complex, scalable data pipelines and infrastructure end-to-end; own the technical design and implementation of data models across relational, NoSQL, and lakehouse architectures to support analytics and ML workloads; architect and optimize data flows across cloud platforms (Azure, AWS, or GCP); build and maintain robust orchestration workflows using Apache Airflow, dbt, or Azure Data Factory; define and implement data quality frameworks with validation rules, automated testing, monitoring, and alerting; collaborate with Data Scientists and Analytics Engineers to ensure data infrastructure meets downstream consumption requirements; contribute to architectural reviews and platform decisions; lead code reviews and enforce engineering best practices, documentation standards, and security and governance requirements; mentor Associate and mid-level Data Engineers; proactively identify platform improvements and opportunities to modernize the data stack; proficient with SQL and Python and working knowledge of Spark/Scala/Java; cloud data services on Azure/AWS/GCP; data orchestration tools and CI/CD practices; data modeling techniques and lakehouse architecture; streaming and batch processing frameworks; data governance and metadata management; strong communication and mentoring abilities; experience with Snowflake, Redshift, or Azure Synapse; 6-8 years of experience; based in Pune, India.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, or a related field
- 6-8 years of progressive hands-on experience in data engineering
- Expert-level SQL and strong Python skills
- Working knowledge of Spark, Scala, or Java
- Deep hands-on experience with cloud data services on Azure, AWS, or GCP (e.g., Azure Data Factory, Synapse, S3, Redshift, BigQuery)
- Advanced knowledge of data orchestration tools (e.g., Apache Airflow, dbt, Prefect) and CI/CD practices for data pipelines
- Solid understanding of dimensional and vault data modelling techniques and lakehouse architecture patterns
- Experience with streaming and batch processing frameworks (e.g., Apache Kafka, Spark Streaming, Azure Event Hubs)
- Strong grasp of data governance, data quality frameworks, and metadata management practices
- Ability to communicate complex technical designs clearly and mentor less experienced engineers effectively
- Strong experience with data warehousing platforms such as Snowflake, Redshift, or Azure Synapse, including performance tuning and access control
- Experience delivering production-grade data infrastructure
- Proven experience designing, building, and optimizing scalable data infrastructure and pipelines
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