Job Summary
Synechron is seeking a skilled ETL Developer with strong expertise in Hadoop ecosystems, Spark, and Informatica to design, develop, and maintain scalable data pipelines supporting enterprise analytics and data warehousing initiatives. This role involves working on large datasets, transforming data, and delivering reliable data integration solutions across on-premise and cloud environments. Your efforts will enable data-driven decision-making, ensure data quality, and support our organization’s strategic focus on scalable and compliant data platforms.
Software Requirements
Required:
Hands-on experience with ETL tools: Informatica, Talend, or equivalent (5+ years)
Proven expertise in Hadoop ecosystem components: HDFS, Hive, Pig, Sqoop (5+ years)
Proficiency in Apache Spark: PySpark, Spark SQL, Spark Streaming
Strong programming skills in Python, Java, or Scala for data processing (5+ years)
Experience with SQL and relational databases: Oracle, MySQL, PostgreSQL
Familiarity with cloud data platforms such as AWS Redshift, Azure Synapse, GCP BigQuery
Preferred:
Knowledge of cloud-native data migration and integration tools
Exposure to NoSQL databases like DynamoDB or Cassandra
Experience with data governance and metadata management tools
Overall Responsibilities
Design, develop, and optimize end-to-end ETL pipelines for large-scale data processing and integrations
Build and enhance batch and real-time data processing workflows using Spark, Hadoop, and cloud services
Convert business and technical requirements into high-performance data solutions aligned with governance standards
Perform performance tuning, debugging, and optimization of data workflows and processing jobs
Ensure data quality, security, and compliance with enterprise standards and industry regulations
Collaborate with data analysts, data scientists, and application teams to maximize data usability and accuracy
Automate data ingestion, transformation, and deployment pipelines for operational efficiency
Support platform stability by troubleshooting issues, monitoring workflows, and maintaining data lineage
Implement and improve data governance, metadata management, and security standards
Stay current with emerging data technologies, automation frameworks, and cloud innovations to optimize data architectures
Technical Skills (By Category)
Programming Languages (Essential):
Python, Scala, Java (for data processing and automation)
Preferred:
Additional scripting or programming skills (Shell, SQL scripting)
Frameworks & Libraries:
Spark (PySpark, Spark SQL, Spark Streaming), Hive, Pig
Data validation and governance tools (e.g., Atlas, Data Catalogs)
AI/ML frameworks such as LangChain, Hugging Face (preferred)
Databases & Storage:
Relational: Oracle, PostgreSQL, MySQL
NoSQL: DynamoDB, Cassandra (preferred)
Cloud Technologies:
AWS: EMR, S3, Glue, CloudFormation, CDK, Redshift (preferred)
Azure or GCP data services (desired)
Data Management & Governance:
Metadata management, data lineage, data quality frameworks
DevOps & Automation:
CI/CD tools: Jenkins, GitHub Actions, TeamCity
Infrastructure as Code: Terraform, CloudFormation, Ansible
Experience Requirements
4+ years of experience in designing and developing large-scale data pipelines
Proven expertise with Hadoop, Spark, and ETL frameworks in enterprise environments
Hands-on experience integrating data within cloud ecosystems and maintaining data quality
Familiarity with regulated industries such as finance or banking is preferred
Demonstrated ability to troubleshoot performance issues and optimize workflows
Day-to-Day Activities
Develop and maintain data pipelines supporting enterprise analytics and reporting
Optimize ETL workflows for performance, scalability, and data accuracy
Collaborate across teams to understand data requirements and implement technical solutions
Automate data processes and manage infrastructure provisioning using IaC tools
Monitor data processing jobs, troubleshoot incidents, and perform root cause analysis
Maintain documentation for data lineage, workflow configurations, and data security
Support migration and platform upgrade projects ensuring minimal disruption
Stay updated on new data processing tools, cloud architecture, and compliance standards
Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field
4+ years managing large-scale data pipelines, preferably in cloud environments
Experience with Hadoop ecosystem, Spark, and ETL tools in enterprise settings
Certifications such as AWS Data Analytics, Cloudera, or relevant data platform certifications are advantageous
Professional Competencies
Strong analytical and troubleshooting skills in data processing contexts
Excellent collaboration and stakeholder management skills
Ability to work independently under deadlines and prioritize tasks effectively
Continuous learning mindset around emerging data, cloud, and AI/ML technologies
Focus on data quality, security, and scalability to meet industry standards
SYNECHRON’S DIVERSITY & INCLUSION STATEMENT
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
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