Azure Data Factory, Databricks and Snowflake Data Engineer

Infosys; Bengaluru; On-site; INR 3,600,000 - 5,200,000; Full time

Job summary

Infosys in Bengaluru is seeking a Senior Data Engineer to own end-to-end data ingestion and orchestration. You will design scalable Spark-based pipelines in Databricks for batch and incremental loads, while integrating Snowflake as a target and ensuring performance and reliability across cloud data platforms.

The role requires 7–9 years of hands-on experience with ADF and Databricks, strong SQL, and the ability to mentor engineers and drive architectural standards within delivery teams.

Qualifications

• 7–9 years of experience building production-grade data pipelines.
• Strong experience with ADF and Databricks for data processing.
• Proven ability to design ETL/ELT patterns with Snowflake as target/source.
• Experience mentoring engineers and guiding design decisions.

Responsibilities

• Lead end-to-end data ingestion and orchestration using ADF with scheduling and error handling.
• Develop scalable Databricks pipelines with Spark-based transformations for batch/incremental loads.
• Build and optimize ELT/ETL patterns with Snowflake for performance and cost efficiency.
• Define data pipeline standards and enforce best practices across the team.
• Implement monitoring, logging, alerting and runbooks for production pipelines.

Skills

Data engineering

Spark transformations

ETL/ELT workflows

Mentorship

Education

BTECH/MTECH/MCA/MSC

Tools

ADF

Databricks

Snowflake

CI/CD for data pipelines

Job description

ADF (Azure Data Factory) + Databricks+ Snowflake Technology->Cloud Integration->Azure Data Factory (ADF) Technology->Data Engineering->Databricks Technology->Data on Cloud->Snowflake Preferred Qualifications:

Preferred Qualifications

• Strong experience integrating and optimizing workloads with Snowflake, including loading strategies and performance tuning.
• Experience implementing medallion/lakehouse-style architectures and scalable data modeling patterns for analytics consumption.
• Familiarity with CI/CD practices for data engineering workflows and automated testing approaches for pipelines.
• Experience with production observability practices (pipeline metrics, logging, alerting) and operational excellence.
• Demonstrated ability to mentor team members, drive design discussions, and influence engineering standards across projects.

Key Responsibilities

• Lead end-to-end implementation of data ingestion and orchestration workflows using ADF, including scheduling, dependency management, parameterization, and error handling.
• Design and develop scalable data processing pipelines in Databricks using Spark-based transformations for batch and incremental loads.
• Build and optimize ELT/ETL patterns integrating Snowflake as a target/source, ensuring performance, reliability, and cost efficiency.
• Define data pipeline standards (naming, modularity, reusability) and enforce engineering best practices across the team.
• Implement monitoring, alerting, and operational runbooks for production pipelines; drive incident triage and root-cause analysis.
• Collaborate with stakeholders to translate requirements into technical designs, estimates, and delivery plans; manage risks and dependencies.
• Conduct code reviews, mentor engineers, and guide technical decisions to ensure maintainable and secure solutions.
• Improve pipeline performance through tuning, partitioning strategies, and efficient data layout/processing approaches.

Minimum Qualifications

• BTECH, MTECH, MCA, or MSC.
• 7–9 years of overall experience with strong hands-on expertise in ADF and Databricks for building production-grade data pipelines.
• Proven experience designing and supporting reliable ETL/ELT workflows, including scheduling, retries, and failure recovery patterns.
• Strong SQL skills and experience working with large datasets and data quality considerations.
• Experience collaborating with cross-functional teams and leading technical delivery with ownership mindset.