Synechron – Data Engineer

Job Description

Synechron is hiring a Data Engineer in Chennai to join its data engineering team and work on scalable, reliable, and high-performance data solutions. The role focuses on designing, developing, testing, and maintaining data pipelines using technologies such as PySpark, Apache Spark, Python, SQL, Cloudera Data Platform, Kafka, HDFS, Hive, and Impala. Data Engineers in this position will work with large and complex datasets while supporting data ingestion, transformation, processing, validation, and delivery across enterprise systems. The role is particularly relevant for professionals with experience in Big Data engineering and distributed computing who want to work on modern data platforms. Strong emphasis is placed on data quality, reliability, availability, performance optimization, monitoring, and production support throughout the data pipeline lifecycle.

A significant part of the position involves building and optimizing ETL and ELT workflows for both structured and unstructured data. Engineers will use PySpark and Apache Spark to process large datasets and will work within the Cloudera Data Platform (CDP) and related Hadoop technologies. The role includes working with distributed storage and processing technologies such as HDFS, Hive, Impala, Kafka, and potentially HBase and Airflow. Engineers will also be expected to improve Spark performance through effective partitioning, joins, caching, resource utilization, and query optimization. Data quality checks, validation rules, reconciliation mechanisms, monitoring, and troubleshooting are important components of the position, ensuring that business teams receive accurate and dependable data from enterprise-scale processing systems.

The Data Engineer will collaborate with data architects, analysts, application developers, DevOps engineers, and business stakeholders to deliver data solutions aligned with organizational requirements. The position also involves supporting data migrations, platform improvements, cloud-native initiatives, CI/CD implementation, automated testing, version control, technical documentation, data lineage, and operational support. Knowledge of AWS, Microsoft Azure, Google Cloud, Docker, Kubernetes, Jenkins, Terraform, Spark SQL, Scala, and modern lakehouse architectures can provide additional value. Experience in financial services, banking, fintech, or another regulated environment is also beneficial because Synechron has a strong foundation in financial services technology. This makes the role suitable for engineers interested in Big Data, cloud data engineering, distributed systems, and enterprise data platforms.

Roles & Responsibilities

• Build scalable data pipelines: Design, develop, test, and maintain robust data pipelines using PySpark, Apache Spark, Python, and SQL to process large volumes of enterprise data efficiently.
• Develop ETL and ELT workflows: Build data transformation processes for structured and unstructured datasets while ensuring that information moves reliably between source systems, processing platforms, and downstream applications.
• Work with Cloudera CDP: Develop data engineering solutions using the Cloudera Data Platform and associated Hadoop ecosystem technologies to support enterprise-scale data processing requirements.
• Use distributed technologies: Work with HDFS, Hive, Impala, Kafka, Spark, and related technologies to build distributed data processing and storage solutions capable of handling large datasets.
• Improve Spark performance: Optimize Spark applications through appropriate partitioning, efficient joins, caching strategies, resource allocation, query optimization, and effective processing techniques.
• Implement data quality controls: Create validation rules, reconciliation processes, data-quality checks, and monitoring mechanisms to identify inaccurate, incomplete, inconsistent, or unexpected data.
• Troubleshoot production issues: Investigate pipeline failures, performance bottlenecks, data inconsistencies, processing errors, and production incidents while working toward reliable and timely resolutions.
• Support data migrations: Participate in data migration initiatives and platform enhancements, ensuring that data is transferred, transformed, validated, and made available according to project requirements.
• Collaborate across teams: Work closely with data architects, analysts, application teams, DevOps engineers, and business stakeholders to understand requirements and develop appropriate data solutions.
• Implement CI/CD practices: Use Git, automated testing, continuous integration, and continuous deployment practices to improve the quality, consistency, and reliability of data engineering releases.
• Maintain documentation: Create and maintain technical documentation covering pipeline architecture, data lineage, processing workflows, operational runbooks, dependencies, and support procedures.
• Follow governance and security: Apply appropriate data security, access-control, governance, compliance, and operational standards while developing and maintaining enterprise data solutions.

Requirements & Eligibility

• Bachelor’s degree: Candidates should have a Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical field that provides a strong foundation in programming and data technologies.
• Data engineering experience: Professional experience as a Data Engineer, Big Data Engineer, or in a closely related data-processing role is expected, particularly experience working with production data systems.
• PySpark and Spark: Strong hands-on experience with PySpark and Apache Spark is important for developing distributed data-processing applications and handling large-scale workloads.
• Python and SQL: Candidates should demonstrate strong programming skills in Python and SQL, including the ability to develop transformation logic, queries, data-processing workflows, and troubleshooting solutions.
• Cloudera experience: Practical experience with the Cloudera Data Platform (CDP) or the broader Cloudera/Hadoop ecosystem is required for working with the organization's distributed data infrastructure.
• ETL/ELT expertise: Candidates should understand how to design, develop, test, monitor, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Big Data technologies: Knowledge of distributed storage and processing technologies such as HDFS, Hive, Impala, Kafka, HBase, and Airflow is valuable for working across different stages of enterprise data workflows.
• Spark optimization: Candidates should understand Spark performance concepts such as partitioning, shuffling, joins, caching, resource utilization, query optimization, and efficient data processing.
• Data quality and monitoring: Experience implementing validation, reconciliation, monitoring, and data-quality processes is important for maintaining trustworthy and reliable data pipelines.
• Development practices: Experience with Git, CI/CD pipelines, automated testing, Agile development, code reviews, and collaborative engineering workflows is expected.

Preferred Skills

• Cloud platforms: Experience with AWS, Microsoft Azure, or Google Cloud can be beneficial for organizations adopting cloud-native data infrastructure and distributed processing environments.
• Additional technologies: Knowledge of Spark SQL, Scala, Docker, Kubernetes, Jenkins, or Terraform can provide additional technical versatility when working with modern data platforms.
• Data governance: Familiarity with data governance, data lineage, metadata management, data security, access controls, and compliance processes is valuable for enterprise data engineering.
• Lakehouse architecture: Exposure to modern data lake, data warehouse, or lakehouse architectures can help candidates understand how organizations organize, process, store, and serve large-scale analytical data.
• Financial services knowledge: Experience in banking, financial services, fintech, or other regulated industries can be advantageous, particularly when working with enterprise data that requires strong governance and security controls.
• Problem-solving ability: Strong analytical thinking and troubleshooting skills are important for identifying root causes of complex data-processing problems and developing reliable solutions.
• Communication skills: Engineers should be able to communicate technical concepts clearly, document solutions effectively, and collaborate with technical and business stakeholders.

Expected Salary

For a Data Engineer in Chennai, a realistic market range is approximately ₹7 lakh to ₹14 lakh per year, depending on experience, technical specialization, company, and total compensation. Engineers with strong PySpark, Spark, Cloudera, Kafka, cloud, and distributed-systems expertise can command compensation toward the higher end of the market.

The actual Synechron package may vary according to the candidate's professional experience, technical assessment, project requirements, seniority, and overall compensation structure. Candidates with specialized Big Data and financial-services technology experience may see different compensation from general Data Engineer roles.

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