Job Summary

We are seeking a skilled Data Engineer to design, develop, and maintain scalable data pipelines and up-to-date data platforms. The ideal candidate will have hands-on experience in data engineering, ETL/ELT processes, cloud technologies, big data frameworks, and data warehousing solutions. The role involves collaborating with data scientists, analysts, and business stakeholders to deliver reliable and high-quality data solutions.

Key Responsibilities

- Design, develop, and maintain scalable data pipelines and data integration solutions.
- Build and optimize ETL/ELT workflows for processing large-scale datasets.
- Develop and manage enterprise data warehouses, data lakes, and lakehouse architectures.
- Integrate data from multiple structured and unstructured sources.
- Ensure data quality, governance, security, and compliance standards.
- Optimize data storage, retrieval, and processing performance.
- Collaborate with Data Scientists, BI Developers, Product Owners, and business teams.
- Monitor and troubleshoot data pipelines and production data issues.
- Implement data validation, lineage, and monitoring frameworks.
- Support real-time and batch data processing requirements.

Required Skills Core Technical Skills
- Strong proficiency in SQL and database design principles.
- Hands-on experience with Python, PySpark, or Scala.
- Experience building ETL/ELT pipelines and data integration solutions.
- Strong understanding of Data Modeling (Star Schema, Snowflake Schema, Dimensional Modeling).
- Experience with Data Warehousing concepts and best practices.
- Knowledge of batch and real-time data processing frameworks.

Big Data Technologies
- Apache Spark
- Hadoop Ecosystem
- Databricks
- Apache Kafka
- Apache Airflow

Cloud Platforms
- Microsoft Azure (ADF, ADLS, Synapse, Fabric)
- AWS (S3, Glue, Redshift, EMR)




- Google Cloud Platform (BigQuery, Dataflow)

Databases
- SQL Server
- PostgreSQL
- Oracle
- MySQL
- MongoDB

Preferred Skills
- Experience with Microsoft Fabric and modern Data Lakehouse architecture.
- Hands-on experience using Azure Data Factory (ADF), Synapse Analytics, and Databricks.
- Experience with streaming technologies such as Kafka and Spark Streaming.
- Knowledge of data governance and cataloging tools.
- Experience with CI/CD implementation for data pipelines.
- Familiarity with Infrastructure as Code (Terraform).

Tools Technologies
- Python
- SQL
- PySpark
- Databricks
- Apache Spark
- Kafka
- Airflow
- Azure Data Factory
- Azure Synapse Analytics
- Microsoft Fabric
- Git
- Azure DevOps
- Jenkins

Educational Qualification
- Bachelors or Masters degree in Computer Science, Information Technology, Data Engineering, or a related field.

Good-to-Have
- Experience working in Retail, E-commerce, Banking, or Enterprise Data Platforms.
- Understanding of Machine Learning data pipelines.
- Knowledge of data governance, data security, and compliance frameworks.
- Robust analytical and problem-solving skills.
- Experience working in Agile/Scrum environments.

Core Responsibility:

ETL/ELT Pipeline Development: Build and maintain data pipelines extracting from Martech Platforms.

MarTech CDP Integration: Manage Customer Data Platforms (CDPs) and associated tools

Identity Resolution Attribution: Manage user stitching, PII-compliant data handling

Data Quality Governance: Ensure data lineage, schema validation, and reliable monitoring using orchestration tools like Apache Airflow

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Engineer (Bengaluru)
🏢 Zensar
📍 Bengaluru