L1 Data Engineer - Remote
DeepSource Technologies Open · verified Oct 24, 2026 At a glance
Mid-level Data Engineering role at DeepSource Technologies. Remote, India · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain data pipelines and ETL/ELT workflows for analytics and business intelligence needs
- Use Azure Data Factory and Databricks to ingest data, keeping it consistent and reliable across platform layers
- Process large datasets with Python and PySpark, and tune SQL for reporting and data checks
- Work with data architects and senior engineers on data models, then troubleshoot pipeline and quality failures
- Document pipelines, data dictionaries and engineering practices, while helping assess infrastructure improvements
What you bring
- At least three years in data engineering or a closely related role
- Strong Python skills, with hands-on Pandas and PySpark experience for data processing
- Practical Databricks experience, including notebooks, clusters and job orchestration, plus Azure Data Factory pipelines
- Working knowledge of Azure Synapse Analytics and Spark pools, alongside solid SQL tuning skills
- You hold or are pursuing the Databricks Certified Data Engineer Associate credential
Who this fits
This role suits a mid-level data engineer who works hands-on with Azure, Databricks, Python and SQL. It is remote in India.
From the employer
We are looking for a motivated and technically solid L1 Data Engineer to join our growing Data & Analytics team. In this role, you will be responsible for designing, building, and maintaining the data architecture and infrastructure that supports our organization's data strategy. You will work hands-on to develop, test, and deploy reliable data solutions — ensuring pipelines are scalable, efficient, and aligned with business requirements.
This is an ideal opportunity for a data professional who is eager to deepen their expertise in cloud-native data platforms, particularly within the Microsoft Azure and Databricks ecosystem, and who thrives in a collaborative, fast-paced environment.
KEY RESPONSIBILITIES
• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows to support business intelligence and analytics use cases.
• Build and optimize data ingestion processes using Azure Data Factory and Databricks, ensuring data quality and consistency across all layers of the data platform.
• Transform and process large datasets using PySpark and Python, applying best practices for performance and maintainability.
• Write and optimize complex SQL queries to support analytical reporting and data validation requirements.
• Collaborate with data architects and senior engineers to implement and maintain data models aligned with organizational standards.
• Monitor, troubleshoot, and resolve pipeline failures and data quality issues, applying root-cause analysis to prevent recurrence.
• Contribute to documentation of data pipelines, data dictionaries, and engineering standards.
• Support the team in exploring and evaluating new tools and approaches to continuously improve the data infrastructure.
Requirements
- 3+ years of professional experience in a Data Engineering or closely related role.
- Strong proficiency in Python for data processing, transformation, and automation tasks.
- Hands-on experience with Pandas for data manipulation and PySpark for distributed data processing.
- Practical experience with Databricks, including notebook development, clusters, and job orchestration.
- Experience building and managing data pipelines with Azure Data Factory.
- Working knowledge of Azure Synapse Analytics, particularly Spark pool integration.
- Solid SQL skills, including query writing, optimization, and performance tuning.
- Familiarity with data engineering principles including incremental loading, data lake architecture, and Delta Lake.
- Understanding of data governance and security concepts within a cloud data platform.
NICE TO HAVE
- Experience with SQL Server migration projects, including schema conversion and data movement.
- Exposure to Terraform for Azure infrastructure provisioning and management.
- Familiarity with CI/CD practices applied to data engineering workflows.
- Experience with Delta Sharing or Lakehouse Federation concepts.
CERTIFICATION REQUIREMENT
- Candidates are expected to hold or be actively working toward the Databricks Certified Data Engineer Associate certification. This certification validates foundational knowledge across the following domains:
- Databricks Lakehouse Platform architecture and capabilities
- ETL and ELT workflows using Spark SQL and PySpark
- Incremental data processing and structured streaming
- Production pipeline development and orchestration
- Data governance and security within the Databricks environment
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