Databricks Data Engineer

About the role

Hitachi Solutions India is looking for a highly experienced Data Engineer for its product organization in Pune, on a full-time basis. The role centres on big data systems design and data architecture, with a focus on Spark and Delta/Data Lake technology. You will help design, develop, enhance and maintain complex data pipeline and API products that support business-critical operations and large-scale analytics.

Key responsibilities

• Work with other engineers to architect and implement complex systems with minimal oversight, and partner with team leadership to improve and expand platform capabilities.
• Design, develop and maintain complex data pipeline products supporting business-critical operations and large-scale analytics.
• Partner with analytics, data science and engineering teams to understand and solve their data needs and challenges.
• Stay current with the latest developments in data engineering and embrace new concepts.
• Enjoy a role with strong independence and autonomy while contributing to the technical maturity of the organization.

Required profile

• 5+ years of data engineering experience, including 4+ years designing and building Databricks data pipelines.
• A robust background in data engineering or software engineering, with the ability to learn new concepts quickly.
• Preferred: Azure cloud experience; AWS, GCP or other cloud platforms considered.

Required skills

• 4+ years of hands-on Python, PySpark or SparkSQL; Scala is a plus.
• 4+ years with big data pipelines or DAG tools such as Airflow, dbt, Data Factory or similar.
• 4+ years of Spark experience, especially Databricks Spark and Delta Lake.
• 5+ years of software development, including Python 3.x, Django/Flask, FastAPI or other standard APIs, and relational databases (SQL/ORM).
• Nice to have: Microsoft Fabric with PySpark, strong SQL, data modelling (Kimball/star schema, medallion architecture), Kafka or EventHub, database deployment pipelines, unit testing or data quality frameworks.

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