{"id":2369476,"url":"https://alion.io/job/iqor-data-integration-engineer-i","title":"Data Integration Engineer I","company":{"id":2395836,"name":"iQor","domain":"iqor.com","url":"https://alion.io/company/iqor-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":11500,"max_usd":27000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"Apache Camel","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Apache NiFi","optional":false},{"name":"BigQuery","optional":false},{"name":"Cassandra","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Informatica","optional":false},{"name":"Java","optional":false},{"name":"MuleSoft","optional":false},{"name":"MySQL","optional":false},{"name":"Oracle","optional":false},{"name":"PowerShell","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"SOAP","optional":false},{"name":"SQL","optional":false},{"name":"Talend","optional":false},{"name":"Apex","optional":true}],"status":"live","first_seen_at":"2026-10-12T16:02:05Z","employer_posted_date":null,"last_verified_at":"2026-10-12T16:02:05Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Job Summary: \nA Data Integration Engineer is responsible for designing, developing, and maintaining data integration solutions within an organization. They work closely with various stakeholders, including business analysts, data scientists, and software developers, to ensure the smooth flow of data between different systems, databases, and applications. Their primary objective is to create efficient, reliable, and scalable data integration pipelines that enable accurate data analysis and reporting.\n\nResponsibilities: \nData Source Identification: Identify and understand the various data sources within the organization, which may include databases, applications, external APIs, flat files, and more.\nData Extraction: Extract data from source systems using ETL (Extract, Transform, Load) processes, or through real-time data streaming technologies.\nData Transformation: Clean, transform, and enrich the data to ensure it meets the quality and formatting requirements of the target system. This may involve data cleansing, data validation, and data standardization.\nData Integration: Integrate data from multiple sources into a central data repository or data warehouse, ensuring that the data is consistent and accurate.\nETL Development: Develop and maintain ETL (Extract, Transform, Load) processes, scripts, and workflows to automate data integration tasks.\nData Mapping.: Create data mapping documents to define how data elements from source systems correspond to data elements in the target system.\nData Quality Assurance: Implement data quality checks and validation rules to identify and correct data quality issues during the integration process.\nPerformance Optimization: Optimize data integration processes for performance and efficiency, especially in scenarios involving large datasets.\nData Modeling: Collaborate with data architects to design and implement data models that support the integration process and ensure data consistency.\nData Governance: Enforce data governance policies and best practices to maintain data integrity, security, and compliance with regulatory requirements.\nError Handling: Implement error handling and logging mechanisms to track and address data integration failures or issues.\nData Synchronization: Ensure data is synchronized between systems, databases, and applications, maintaining up-to-date information across the organization.\nData Security: Implement data security measures, including encryption and access controls, to protect sensitive data during the integration process.\nMonitoring and Maintenance: Monitor data integration processes, schedule and automate data refreshes, and perform routine maintenance to ensure data accuracy and availability.\nDocumentation: Maintain detailed documentation of data integration processes, ETL workflows, and data transformation rules for reference and troubleshooting.\nCollaboration: Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and ensure that integrated data meets their needs.\nTroubleshooting: Investigate and resolve data integration issues, including troubleshooting errors, discrepancies, or performance bottlenecks.\nData Performance Analysis: Analyze data integration performance metrics and make recommendations for improvements.\nTesting: Conduct thorough testing of data integration processes to ensure data accuracy and consistency.\nStay Informed: Stay up-to-date with emerging data integration technologies and best practices to continuously improve data integration processes.\n\nSkills Requirements: \nData Integration Tools: Proficiency in using data integration tools and platforms, such as Apache Nifi, Talend, Informatica, Apache Camel, or similar tools to facilitate data movement and transformation.\nETL (Extract, Transform, Load): Strong knowledge of ETL processes and tools to extract data from source systems, transform it into the desired format, and load it into target systems.\nData Modeling: Understanding of data modeling concepts and experience with techniques like entity-relationship modeling, star schema, and snowflake schema to design effective data structures.\nDatabase Skills: Proficiency in working with relational databases (e.g., SQL Server, Oracle, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra) for data extraction, transformation, and loading.\nProgramming Languages: Knowledge of programming languages like Python, Java, or Scala to develop custom data integration solutions and scripts for data manipulation.\nAPI Integration: Experience in integrating data through web services, RESTful APIs, SOAP, and other integration methods.\nData Transformation: Expertise in data transformation techniques, including data cleansing, data enrichment, and data validation.\nData Quality and Governance: Understanding of data quality and governance principles to ensure data accuracy, consistency, and compliance with organizational standards.\nData Formats: Familiarity with various data formats, such as XML, JSON, CSV, and Parquet, and the ability to work with them effectively.\nData Security: Knowledge of data security best practices and encryption techniques to protect data during integration processes.\nVersion Control: Proficiency in using version control systems like Git to manage and track changes in data integration code and configurations.\nMonitoring and Troubleshooting: Ability to set up monitoring tools and proactively identify and address data integration issues and bottlenecks.\nData Warehousing: Understanding of data warehousing concepts and experience with data warehousing platforms, such as Amazon Redshift, Snowflake, or Google BigQuery.\nScripting and Automation: Proficiency in scripting and automation tools (e.g., Shell scripting, PowerShell) to streamline data integration workflows.\nCollaboration and Communication: Effective communication skills to collaborate with other teams and stakeholders, understand their data requirements, and translate them into integration solutions.\nProject Management: Basic project management skills to plan, execute, and track data integration projects and ensure they meet deadlines and objectives.\nDocumentation: Strong documentation skills to create and maintain clear, comprehensive documentation of data integration processes, configurations, and transformations.\nAdaptability: The ability to adapt to new technologies, tools, and data integration methods as the field evolves.\nAnalytical Thinking: Strong analytical and problem-solving skills to identify and resolve data integration issues efficiently.\nTeamwork: The ability to work effectively in a team environment and collaborate with data engineers, data analysts, and other IT professionals.\n\nEducation Requirements: \nEducational Background:\nBachelor's Degree: A bachelor's degree in a related field is typically required. While there isn't a specific major for Data Integration Engineering, degrees in Computer Science, Information Technology, Software Engineering, or Data Science are commonly relevant.\nRelevant Courses: Your coursework should include topics such as database management, data modeling, data warehousing, ETL (Extract, Transform, Load) processes, and programming languages like SQL, Python, or Java.\n\nExperience:\nInternships or Entry-Level Positions: Gaining practical experience through internships, co-op programs, or entry-level positions can be invaluable. These roles can help you learn the ropes and gain hands-on experience with data integration tools and processes.\n\nTechnical Skills:\nDatabase Knowledge: A strong understanding of databases, including relational and non-relational databases, is essential.\nETL Tools: Familiarity with ETL tools like Apache Nifi, Talend, Informatica, or Apache Kafka is often required.\nScripting/Programming: Proficiency in scripting or programming languages like SQL, Python, or Java is crucial.\nData Formats: Knowledge of various data formats (XML, JSON, CSV) and experience in transforming data between them.\nData Warehousing: Understanding data warehousing concepts and technologies is important.\n\nData Integration Tools: Familiarity with data integration tools and platforms, such as Apache Nifi, Talend, Informatica, Apache Kafka, MuleSoft, or others, is often expected.\n\nSoft Skills:\nProblem-Solving: Strong problem-solving skills to handle data integration challenges effectively.\nCommunication: Effective communication is essential, as Data Integration Engineers often need to work closely with business stakeholders to understand data requirements.\nTeamwork: Collaboration with various teams within an organization is common, so being a team player is important.\n\nCertifications: While not always required, obtaining relevant certifications in data integration tools or related technologies can be beneficial and demonstrate your expertise.\n\nContinuous Learning: Data integration technology evolves rapidly. A commitment to ongoing learning and staying current with industry trends is essential.\n\nAdvanced Degrees (Optional): Some Data Integration Engineers pursue master's degrees in fields like Data Science, Information Systems, or Business Analytics to deepen their knowledge and open up higher-level career opportunities.\n\nPhysical Requirements: \nOccasionally exert up to 10 lbs. of force to push, pull, lift or otherwise move objects. Have visual acuity to perform activities such as preparing and analyzing data and/or viewing a computer terminal. Type and/or sit for extended periods of time. 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