Data Engineer – Data Engineering & Analytics

Experience: At least 3 years
Employment Type: Full-time

Job Overview

We are seeking an experienced Data Engineer with at least 3 years of relevant experience in designing, developing, and maintaining scalable data pipelines and data platforms. The ideal candidate will have hands-on experience with modern data engineering technologies, cloud-based data solutions, and data processing frameworks.

This role will focus on building reliable data infrastructure, optimizing data workflows, supporting analytics initiatives, and ensuring data quality and governance across the organization.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines for batch and real-time data processing.
• Build and optimize data platforms and workflows using technologies such as Databricks, Apache Spark, Snowflake, and Kafka.
• Develop and manage data integration and transformation processes using dbt, Azure Data Factory, AWS Glue, or similar tools.
• Design and maintain data architectures that support reporting, analytics, and business intelligence requirements.
• Implement data modeling, data transformation, and data warehousing solutions.
• Monitor and optimize data pipeline performance, reliability, and cost efficiency.
• Establish and maintain data quality checks, validation processes, and monitoring mechanisms.
• Support data governance initiatives, including data lineage, documentation, access controls, and compliance.
• Collaborate with data scientists, analysts, software engineers, and business stakeholders to understand data requirements.
• Troubleshoot data processing issues and implement solutions to improve pipeline reliability.
• Document data architectures, workflows, technical processes, and best practices.

Required Qualifications

• At least 3 years of relevant professional experience in data engineering, data platform development, analytics engineering, or a related field.
• Strong SQL skills and proficiency in Python or another programming language used for data processing.
• Hands-on experience building and maintaining data pipelines and ETL/ELT workflows.
• Experience with one or more cloud data platforms, such as Databricks, Snowflake, Azure, or AWS.
• Knowledge of data processing frameworks such as Apache Spark.
• Experience with data transformation tools such as dbt or equivalent technologies.
• Understanding of data warehousing, data modeling, and database concepts.
• Familiarity with data integration tools such as Apache Kafka, Azure Data Factory, or AWS Glue.
• Knowledge of data quality, validation, and governance principles.
• Strong analytical, problem-solving, and troubleshooting skills.

Preferred Qualifications

• Experience working with large-scale data platforms and distributed processing environments.
• Familiarity with cloud-based data lake and lakehouse architectures.
• Experience implementing automated data quality checks and monitoring.
• Knowledge of data orchestration tools and workflow scheduling.
• Understanding of data security, access management, and regulatory compliance.
• Experience supporting business intelligence, advanced analytics, or machine learning initiatives.

Technical Skills and Platforms

• Data Platforms: Databricks, Snowflake
• Processing Frameworks: Apache Spark, Apache Kafka
• Data Transformation: dbt
• Cloud Data Services: Azure Data Factory, AWS Glue
• Programming and Querying: Python, SQL
• Core Expertise: ETL/ELT, data modeling, data warehousing, data quality, data governance

Ideal Candidate Profile

The ideal candidate is a hands-on data professional who can build reliable and scalable data solutions, work with diverse data sources, and ensure that data is accurate, accessible, and ready for analytics and business use. They should be comfortable collaborating with technical and non-technical stakeholders to deliver effective data solutions.

Industry Experience

Experience in one or more of the following industries is advantageous:

• Retail and e-commerce
• Banking, Financial Services, and Insurance (BFSI)
• Healthcare
• Logistics and supply chain
• Manufacturing
• Global Capability Centers (GCCs)

Work Location: Remote