Freelance Senior Data Engineer – Cloud, ETL/ELT & Data Engineering

Freelance Senior Data Engineer – Cloud, ETL/ELT & Data Engineering

Job Type: Freelance / Independent Contractor
Work Arrangement: Remote
Engagement Type: Project-Based / Flexible Hours
Experience Required: 7–10+ Years of Relevant Experience

Job Overview

We are seeking an experienced Freelance Senior Data Engineer with at least 7–10 years of relevant hands-on experience in designing, developing, implementing, and maintaining scalable data engineering solutions.

The ideal candidate will have strong expertise in data pipelines, ETL/ELT development, cloud-based data platforms, data warehousing, big data technologies, and workflow orchestration. Candidates should be capable of independently handling technical assignments, troubleshooting complex data issues, and supporting enterprise-level data engineering projects.

This opportunity is suitable for experienced professionals interested in flexible, remote, freelance, and project-based technical engagements.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines for batch and real-time data processing.
• Build and optimize ETL/ELT workflows for ingesting, transforming, validating, and integrating data from multiple sources.
• Develop complex SQL queries and data transformation logic to support analytics, reporting, and business intelligence.
• Implement cloud-based data engineering solutions across AWS, Microsoft Azure, and Google Cloud Platform (GCP).
• Develop and maintain data lakes, data warehouses, lakehouses, and enterprise data platforms.
• Work with distributed data processing frameworks to handle large-scale structured and unstructured datasets.
• Implement workflow orchestration, scheduling, monitoring, and automated pipeline recovery.
• Optimize data pipeline performance, query execution, resource utilization, and cloud infrastructure costs.
• Implement data quality checks, validation rules, metadata management, lineage, and governance controls.
• Support CI/CD, version control, infrastructure automation, and deployment processes for data engineering solutions.
• Troubleshoot production data issues, pipeline failures, data inconsistencies, and performance bottlenecks.
• Collaborate with data architects, analytics engineers, data scientists, and business stakeholders to deliver project requirements.
• Prepare technical documentation and provide regular updates on assigned project deliverables.

Key Data Engineering Tools & Technologies

1. Programming & Query Languages

• Python, PySpark, SQL, Scala
• Advanced SQL, stored procedures, query optimization
• Bash / Shell scripting

2. Cloud Data Platforms

• AWS: S3, Glue, EMR, Redshift, Athena, Lambda, Kinesis
• Microsoft Azure: Azure Data Factory, Azure Databricks, ADLS Gen2, Synapse Analytics, Microsoft Fabric
• GCP: BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage

3. Big Data & Distributed Processing

• Apache Spark, PySpark
• Hadoop, Hive
• Apache Kafka
• Apache Flink
• Delta Lake, Apache Iceberg, Apache Hudi

4. ETL/ELT & Data Integration

• Informatica, Talend, SSIS
• Fivetran, Airbyte
• dbt (data build tool)
• Azure Data Factory, AWS Glue
• Matillion and similar data integration platforms

5. Data Warehousing & Lakehouse Technologies

• Snowflake
• Databricks Lakehouse
• Amazon Redshift
• Google BigQuery
• Azure Synapse Analytics
• Microsoft Fabric
• Teradata and Oracle, where applicable

6. Workflow Orchestration & Scheduling

• Apache Airflow
• Dagster
• Prefect
• Azure Data Factory Pipelines
• AWS Step Functions
• Cloud Composer

7. Data Modeling, Quality & Governance

• Dimensional modeling, star and snowflake schemas
• Data vault and enterprise data modeling
• Great Expectations, Soda
• Data lineage, metadata management, cataloging, and governance
• Data quality monitoring and reconciliation

8. DevOps & Infrastructure Automation

• Git, GitHub, GitLab, Azure DevOps
• Docker and Kubernetes
• Terraform
• CI/CD pipelines
• Infrastructure as Code (IaC)
• Automated testing, deployment, and release management

Required Qualifications

• 7–10+ years of relevant professional experience in data engineering, data integration, or data platform development.
• Strong hands-on programming experience with Python, PySpark, and SQL.
• Proven experience building and supporting production-grade ETL/ELT pipelines.
• Experience with at least one major cloud platform: AWS, Azure, or GCP.
• Hands-on experience with modern data warehouses, lakehouses, or big data processing platforms.
• Strong understanding of data modeling, distributed processing, performance optimization, and data quality.
• Experience with orchestration tools, monitoring, troubleshooting, and production support.
• Familiarity with Git, CI/CD, automated testing, and deployment best practices.
• Ability to work independently, communicate effectively, and deliver assigned technical tasks within agreed timelines.

Preferred Qualifications

• Experience designing enterprise-scale data platforms and modernizing legacy data pipelines.
• Knowledge of streaming data architectures and real-time data processing.
• Experience with data governance, security, access controls, and regulatory compliance requirements.
• Familiarity with master data management (MDM), metadata platforms, and data observability.
• Relevant cloud or data engineering certifications are preferred but not mandatory.

Compensation & Engagement

Compensation will be negotiated based on experience, technical specialization, project complexity, and mutually agreed hourly or project-based rates.

The engagement may vary depending on project availability, duration, and client requirements.

How to Apply

Interested candidates should submit an updated resume along with the following information:

• Total years of experience and relevant data engineering experience
• Primary cloud platform and data engineering specialization
• Key tools and technologies used in recent projects
• Experience with production pipelines and enterprise data platforms
• Relevant certifications, if any
• Current availability
• Expected hourly rate or project-based compensation

We welcome experienced Senior Data Engineers who are interested in flexible, remote freelance opportunities and project-based technical assignments.

Pay: ₹5,000.00 - ₹10,000.00 per week

Benefits:

• Flexible schedule

Work Location: Remote