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Senior Data Engineer

SRM Technologies
Remote, IND
Full-time
Senior · 8+ years experience
Remote
Discovered 2 weeks ago
PythonPySparkApache SparkSnowflakedbtApache Iceberg

Full Job Posting

Summary

Role: Senior Data Engineer

Experience: 8+ Years

Mandatory/Core: Python, PySpark, Snowflake, dbt, Apache Iceberg, AWS, SQL

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

Role Type: Senior hands-on Data Engineer

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

Senior Data Engineer

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

Primary Skills

  • Python
  • PySpark / Apache Spark
  • Snowflake
  • dbt (Data Build Tool)
  • Apache Iceberg
  • AWS Data Services
  • Advanced SQL
  • Data Engineering / ETL / ELT
  • 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
  • CI/CD for data pipelines
  • Git / GitHub / GitLab
  • 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.

1. Data Pipeline Engineering

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 .

Develop modular, reusable, maintainable dbt models.

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.

4. Apache Iceberg / Lakehouse

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.

5. AWS Data Engineering

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.

8. Engineering Best Practices

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:

Python

Advanced, production-quality data engineering development

PySpark

Large-scale distributed processing, optimization and troubleshooting

Snowflake

Development, modeling, optimization and performance tuning

dbt

Models, tests, macros, documentation and deployment practices

Apache Iceberg

Lakehouse/table design, partitioning, schema evolution and optimization

AWS

Hands-on cloud data platform development

SQL

Advanced SQL, query optimization and analytical processing

Data Engineering

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

Data Architecture

Data Lake, Data Warehouse and Lakehouse concepts

Engineering Practices

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.
  • Strong experience delivering enterprise-scale cloud data platforms .
  • 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.

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