Senior Data Engineer (Snowflake / Databricks)

Ahmedabad, Pune

5+ years

We are looking for a highly skilled Senior Data Engineer with strong experience in modern data engineering and cloud data platforms. The ideal candidate will have hands-on experience with Snowflake or Databricks, along with a strong understanding of data engineering, ETL/ELT processes, data modelling, data warehousing, and cloud platforms.
Hands-on experience with at least one of Snowflake or Databricks is mandatory, while working knowledge of the other platform is good to have. The candidate will be responsible for designing, developing, optimizing, and maintaining scalable data pipelines and data solutions.
The role will involve working closely with data engineers, data scientists, analysts, and business teams to deliver reliable and high-quality data solutions.

Key Responsibilities:

Data Architecture & Development: Design, develop, and optimize scalable, secure, and high-performance data solutions using Snowflake and/or Databricks.
ETL/ELT Pipeline Engineering: Build and maintain robust data pipelines with appropriate orchestration, monitoring, retries, logging, and error handling.
Data Modelling & Transformation: Develop scalable data models and transformations following data warehousing and lakehouse best practices.
Snowflake / Databricks Development: Develop and maintain data solutions using Snowflake or Databricks and optimize data processing and query performance.
Advanced Data Engineering & Optimization: Develop and optimize complex SQL and PySpark transformations for large-scale batch and incremental processing. Implement CDC, merge/upsert patterns, partitioning, and schema evolution while performing hands-on tuning of Snowflake queries, Spark jobs, and Delta Lake workloads. Build pipeline observability through execution logging, source-to-target reconciliation, data completeness checks, error diagnostics, and automated failure handling.
Data Quality: Implement data validation, automated testing, monitoring, and quality frameworks to ensure data integrity and reliability.
Performance Optimization: Monitor pipeline and job performance, troubleshoot issues, and optimize queries and data processing workloads.
Cloud Data Engineering: Develop and manage data pipelines and solutions on AWS, Azure, or GCP.
Collaboration: Work with data analysts, data scientists, and business stakeholders to translate requirements into technical specifications and deliverables.
Technical Contribution: Participate in code reviews, technical discussions, design reviews, and establish data engineering best practices.
Innovation & Research: Stay updated on advancements in Snowflake, Databricks, cloud data platforms, AI/ML, and modern data engineering practices.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, or a related field.
• 5+ years of experience in data engineering, with hands-on experience in at least one of Snowflake or Databricks.
• Strong understanding of:
  • Data modelling
  • Data warehousing
  • ETL/ELT processes
  • Data Lake / Lakehouse architectures
  • Data quality and validation
• Snowflake: Hands-on experience with data modelling, query/performance optimization, access control, streams, tasks, external tables, or equivalent capabilities.
• Databricks: Hands-on experience with data engineering, Spark, Delta Lake/Lakehouse concepts, and data pipeline development.
• Snowflake or Databricks – one must be hands-on; knowledge of the other is good to have.
  Proficiency in SQL and Python. Spark experience is expected for candidates with Databricks exposure.
• Experience building data pipelines on cloud platforms such as AWS, Azure, or GCP.
• Experience with orchestration tools such as Apache Airflow, Azure Data Factory, AWS Glue, or equivalent.
• Familiarity with dbt and modern ELT practices is good to have.
  Familiarity with Git and CI/CD practices.
• Strong analytical, problem-solving, and communication skills.
• Ability to work independently as well as collaboratively in a team environment.

Preferred Skills:

• Hands-on experience with both Snowflake and Databricks.
• Experience with Apache Airflow and dbt.
• Experience with streaming data pipelines such as Kafka, Kinesis, or Pub/Sub.
• Exposure to Generative AI, ML, or advanced analytics solutions.
• Familiarity with BI/analytics tools such as Power BI, Tableau, or Looker.
• Knowledge of data governance, security, and compliance best practices.
• Familiarity with Terraform or other infrastructure-as-code tools.
• Exposure to multiple cloud platforms such as AWS, Azure, or GCP.

Perks

• Flexible Timings
• 5 Days Working
• Healthy Environment
• Celebration
• Learn and Grow
• Build the Community
• Medical Insurance Benefit

Learn and Grow

Work on real Data and AI problems.

Build skills that matter.
Grow every year.

Flexible Work Culture

Five-day work week.
Flexible timings.
Healthy, people-first environment.

Strong Teams and Community

Collaborative teams.
Open culture.
Celebrations that bring people together.

Stability and Care

Medical insurance.
Long-term growth.
A company that genuinely cares.