Senior Data Engineer

Preferred:

AWS Glue, S3, EMR, Lambda, Airflow, Snowpipe/Snowpark, CI/CD, Terraform, Data Modeling

Focus:

Cloud Data Engineering, Lakehouse, Data Transformation, Performance Optimization and Production Engineering

Detailed information:

Experience: 8+ years of overall Data Engineering experience , with strong hands-on experience building enterprise-scale cloud data platforms and pipelines.

Primary Skills:

• Python
• Snowflake
• dbt (Data Build Tool)
• AWS Data Services
• Advanced SQL
• Data Lake / Lakehouse architecture

Secondary / Preferred Skills:

• AWS services such as:
• S3
• AWS Glue
• EMR
• Lambda
• Step Functions
• CloudWatch
• IAM
• Apache Airflow or other workflow orchestration tools
• Snowflake performance optimization and cost optimization
• Snowpipe / Snowpark
• Spark performance tuning
• Data modeling and dimensional modeling
• Parquet and other columnar data formats
• Data quality frameworks and automated validation
• Infrastructure as Code such as Terraform or AWS CDK
• Docker / containerization
• Data governance, lineage, security, and access control
• Agile/Scrum delivery experience

Job Description:

We are looking for a Senior Data Engineer with strong hands-on expertise in Python, PySpark, Snowflake, dbt, Apache Iceberg, and AWS to design, develop, and maintain scalable enterprise data solutions.

The candidate should have strong experience working with high-volume data processing, cloud-based data platforms, modern lakehouse architectures, ETL/ELT pipelines, data modeling, performance optimization, and production-grade engineering practices.

The ideal candidate should be capable of independently owning complex data-engineering components, contributing to technical design and architecture decisions, troubleshooting production issues, and providing technical guidance to other engineers.

Key Responsibilities:

• Design, develop, test, and maintain scalable ETL/ELT data pipelines.
• Develop production-quality data-processing solutions using Python and PySpark.
• Build reusable frameworks and components for ingestion, transformation, validation, and publishing of data.
• Process large structured, semi-structured, and distributed datasets.
• Implement incremental and batch-processing patterns where appropriate.

2. Snowflake Development

• Design and develop scalable data solutions using Snowflake.
• Develop complex SQL transformations, data models, views, and reusable data structures.
• Optimize Snowflake workloads for performance, scalability, and cost.
• Implement appropriate data-loading and transformation patterns between AWS data platforms and Snowflake.
• Troubleshoot performance and data-quality issues across Snowflake workloads.

3. dbt Development

• Build and maintain transformation pipelines using dbt.
• Implement dbt tests and documentation.
• Follow appropriate development practices for source, staging, intermediate, and business-layer transformations.
• Support automated deployment and CI/CD practices for dbt projects.
• Design and implement data-lake and lakehouse solutions using Apache Iceberg.
• Build scalable table structures for large analytical datasets.
• Work with partitioning, schema evolution, incremental processing, and table-maintenance strategies.
• Integrate Iceberg-based datasets with Spark and AWS-based data-processing services.
• Ensure efficient storage and query patterns for high-volume datasets.
• Design and implement cloud-native data solutions on AWS.
• Build data-processing workloads leveraging services such as S3, Glue, EMR and Lambda where appropriate.
• Implement secure access patterns using AWS IAM.
• Monitor data workloads and troubleshoot operational issues.
• Participate in designing scalable, reliable, secure, and cost-efficient cloud data architectures.

6. Performance & Scalability

• Diagnose and optimize Spark/PySpark jobs , SQL queries, Snowflake workloads, and data pipelines.
• Identify bottlenecks involving compute, storage, partitioning, data skew, transformations, and queries.
• Design solutions capable of supporting increasing data volumes without unnecessary infrastructure cost.

7. Data Quality & Governance

• Implement automated data-quality checks across ingestion and transformation layers.
• Establish proper logging, monitoring, exception handling, and reconciliation mechanisms.
• Follow organizational standards for data security, governance, lineage, and access controls.
• Ensure production pipelines are reliable, auditable, and maintainable.
• Write clean, modular, reusable, testable, and maintainable code.
• Perform code reviews and enforce engineering standards.
• Implement unit, integration, and data-validation testing.
• Use Git-based version control and CI/CD practices.
• Create and maintain appropriate technical documentation.

9. Senior-Level Responsibilities

• Independently drive technically complex data-engineering requirements from design through production deployment.
• Participate in solution design and architecture discussions.
• Evaluate alternative implementation approaches and recommend appropriate solutions.
• Troubleshoot complex production and performance issues.
• Mentor junior and mid-level data engineers.
• Collaborate with Architects, Product Owners, Business Analysts, Data Scientists, QA, DevOps, and application teams.
• Translate business/data requirements into scalable technical solutions.
• Identify technical risks and proactively recommend improvements.

Core Skills Expected

A strong candidate should demonstrate deep hands-on capability , not merely theoretical exposure, in the following areas:

Area

Expected Capability

Python

PySpark

Large-scale distributed processing, optimization and troubleshooting

Snowflake

Development, modeling, optimization and performance tuning

dbt

Models, tests, macros, documentation and deployment practices

Lakehouse/table design, partitioning, schema evolution and optimization

AWS

SQL

Advanced SQL, query optimization and analytical processing

ETL/ELT, batch/incremental pipelines, data quality and orchestration

Data Lake, Data Warehouse and Lakehouse concepts

Git, testing, code reviews, CI/CD and production support

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.
• Experience migrating legacy data workloads to modern AWS/Snowflake architectures.
• Experience working with very large datasets and distributed processing.
• Knowledge of data security and governance practices.
• Experience working in Agile delivery environments.
• AWS and/or Snowflake certification is an added advantage.