Cloud Data Engineer

Job Title: Cloud Data Engineer – Databricks, dbt Cloud & AWS

Experience: 5–8 Years Key Experience: 5+ years in Databricks and 3+ years in cloud-based data lake/lakehouse architecture Education: Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field

Job Summary

We are looking for an experienced Cloud Data Engineer with strong hands-on expertise in Databricks, dbt Cloud, and AWS to help build, maintain, and optimize scalable enterprise data platforms.

The ideal candidate will have experience in data engineering, data warehousing, cloud data lake architecture, and enterprise-scale ETL/ELT pipelines. This role involves modernizing legacy data warehouse implementations, converting SQL and PL/SQL business logic into scalable Spark SQL transformations, and developing modular data products using dbt Cloud and dbt Mesh.

The candidate will also contribute to platform reliability, performance tuning, cost optimization, data governance, and automated deployment workflows.

Key Responsibilities

• Analyze existing data warehouse implementations and support migration and consolidation initiatives.
• Reverse-engineer legacy SQL and PL/SQL stored procedures and convert business logic into scalable Spark SQL transformations within Databricks.
• Design and develop AWS-based data lake and lakehouse solutions using S3, Databricks, and Delta Lake.
• Build, maintain, and optimize robust ETL/ELT pipelines and data ingestion frameworks.
• Develop reusable transformation pipelines using dbt Cloud, including models, tests, macros, incremental models, snapshots, seeds, and documentation.
• Design and manage multi-project dbt Mesh architectures, including cross-project references, model contracts, model versioning, and access controls.
• Configure and maintain dbt Cloud environments, jobs, deployment schedules, and CI checks across development, QA, and production.
• Optimize data models, including Star Schema, Snowflake Schema, flattened models, and modern lakehouse approaches.
• Tune Spark and Databricks workloads through partitioning, query optimization, performance improvements, and cost management.
• Troubleshoot and resolve technical issues related to data lakes, pipelines, scheduled jobs, and data processing.
• Perform foundational data administration activities, including job scheduling, error troubleshooting, and backup coordination.
• Collaborate with data architects and cross-functional teams to align ingestion and transformation frameworks with enterprise standards.
• Maintain GitHub repositories, manage branching and merging strategies, review pull requests, resolve merge conflicts, and support release management.
• Build and maintain CI/CD workflows using tools such as GitHub Actions for automated testing and deployment of dbt and Databricks code.
• Document ETL processes, data flows, transformation logic, and technical solutions.
• Participate in Agile ceremonies, sprint planning, retrospectives, backlog grooming, and iterative delivery.

Required Skills and Qualifications

• 5–8 years of overall experience in data engineering, data warehousing, or big data platform development.
• 5+ years of hands-on experience with Databricks, including Spark SQL, PySpark, Delta Lake, Unity Catalog, Workflows/Jobs, and performance optimization.
• Strong practical experience with dbt Cloud, including models, tests, Jinja macros, incremental models, snapshots, jobs, environments, and deployments.
• Experience with dbt Mesh, including multi-project architecture, cross-project references using ref(), model contracts, versioning, and governance/access controls.
• Strong knowledge of AWS data services, including S3, Glue, Lambda, IAM, Step Functions, and CloudWatch; exposure to Redshift or EMR is beneficial.
• At least 3 years of experience designing and implementing cloud-based data lake or lakehouse architectures using Databricks.
• Strong understanding of Spark technologies, including Spark SQL, PySpark, or Scala.
• Advanced SQL and PL/SQL skills, including the ability to analyze, understand, and refactor legacy stored procedures.
• Strong understanding of data modeling, data warehousing, and enterprise-scale ETL/ELT pipeline development.
• Proficiency in at least one programming language: Python, Scala, or Java.
• Experience with performance tuning, partitioning strategies, scalability, and cloud cost optimization.
• Strong GitHub skills, including branching, merging, pull requests, code reviews, and repository management.
• Working knowledge of CI/CD and DevOps practices for data engineering workflows, preferably using GitHub Actions.
• Experience working in Agile project environments.
• Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related discipline.

Preferred Qualifications

• Databricks Associate or Professional certification.
• dbt Analytics Engineering certification.
• Experience with enterprise data governance, metadata management, and data catalog platforms such as Unity Catalog.
• Exposure to AWS Redshift and EMR.

What We Are Looking For

A hands-on Cloud Data Engineer who can modernize legacy data platforms, develop reliable data products, implement scalable dbt and Databricks solutions, and collaborate effectively with architects and cross-functional teams.

How to Apply

Interested candidates can share their updated resume at hr@spasnik.com or reach out via direct message.

Pay: ₹1,500,000.00 - ₹2,500,000.00 per year

Work Location: Hybrid remote in Hyderabad, Telangana

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