TE

Data Engineering Sr. Analyst

Teamware Solutions
Bangalore4-7 LPA Posted 16 Jul 2025
FULL TIME
Apache Spark
Talend
Data Security
Api
Informatica

Job Description

  • Key Responsibilities:
  • Data Pipeline Development:
  • Design, develop, and implement scalable, efficient data pipelines to ingest, transform, and load data from various sources (e.g., databases, flat files, APIs, cloud services).
  • Work with ETL tools (e.g., Apache Spark, Talend, Informatica) to extract, transform, and load data into the organization's data warehouse or data lake.
  • Optimize data pipelines for speed, scalability, and cost-efficiency, ensuring that they can handle large volumes of data.
  • Data Architecture & Design:
  • Collaborate with data architects and engineers to design and implement data storage solutions (e.g., data lakes, data warehouses, NoSQL databases) that support business intelligence and data analytics.
  • Create and maintain data models, schemas, and data dictionaries to ensure data consistency and integrity across different systems.
  • Ensure the application of best practices for data governance, including data quality, metadata management, and data security.
  • Data Integration:
  • Integrate diverse datasets from internal and external sources (e.g., third-party APIs, cloud-based systems) into the data environment, ensuring compatibility and consistency.
  • Develop and maintain automated data integration workflows, ensuring smooth and reliable movement of data between systems.
  • Perform data cleansing and transformation to ensure high-quality data for reporting, analysis, and decision-making.
  • Big Data & Cloud Solutions:
  • Work with cloud data platforms (e.g., AWS Redshift, Azure Synapse Analytics, Google BigQuery) to build scalable and efficient data storage solutions.
  • Implement big data technologies such as Hadoop, Spark, and Kafka to handle large datasets and enable real-time data processing.
  • Optimize and manage cloud-based data services for optimal performance, scalability, and cost-effectiveness.
  • Collaboration & Cross-functional Support:
  • Collaborate with data scientists, business analysts, and other stakeholders to understand their data requirements and deliver solutions that meet business needs.
  • Provide technical support to data analysts and data scientists, ensuring they have the data and tools needed to perform their tasks efficiently.
  • Work closely with stakeholders to translate business requirements into technical specifications and data solutions.
  • Data Quality & Performance Monitoring:
  • Monitor and maintain data pipeline performance, troubleshoot issues, and resolve any data quality issues to ensure high data accuracy and reliability.
  • Implement monitoring and alerting solutions to track pipeline performance and data quality, ensuring proactive issue resolution.
  • Conduct regular performance tuning and optimization of data systems and queries to enhance speed and efficiency.
  • Documentation & Reporting:
  • Create and maintain technical documentation for data pipelines, architecture, and processes, ensuring that they are clear, concise, and accessible to the team.
  • Develop reports and dashboards to communicate data quality, pipeline status, and other key metrics to stakeholders and management.
  • Ensure that all data work is thoroughly documented and adheres to compliance and data governance policies.
  • Required Qualifications:
  • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
  • 3-5 years of experience in data engineering, data architecture, or related fields.
  • Proficiency in SQL, including the ability to write complex queries and optimize query performance.
  • Experience with ETL tools such as Apache Spark, Talend, Informatica, or custom ETL frameworks.
  • Strong knowledge of big data technologies like Hadoop, Kafka, Spark, or Flume.
  • Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform, particularly for data storage and processing solutions.
  • Experience with data warehousing solutions such as Redshift, Snowflake, or BigQuery.
  • Understanding of data integration, data modeling, and data governance best practices.
  • Strong problem-solving skills with the ability to troubleshoot data issues and optimize systems for performance.

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