Senior Engineer, Data Engineering
3 weken geleden
Tata Consultancy Services (TCS) is a global leader in IT services, digital, and business solutions that partners with its clients to simplify, strengthen, and transform their businesses. We ensure the highest levels of certainty and satisfaction through a deep-set commitment to our clients, comprehensive industry expertise, and a global network of innovation and delivery centers.
TCS operates on a global scale & has over 6,08,985 of the world’s best-trained consultants representing 152 nationalities in 53 countries. TCS established its presence in the Netherlands in 1992 and works with leading Dutch Customers across industry sectors, including several NL20 companies. TCS has been ranked #1 in customer satisfaction, among IT services providers in the Netherlands in a survey by Whitelane Research and is recognized as a Top Employer in the Netherlands, Europe & also Global by the Top Employers Institute
A Data Engineer is responsible for designing, building, and maintaining the infrastructure that enables data collection, storage, and analysis. They work closely with data scientists, analysts, and other engineers to ensure that large datasets are accessible, reliable, and properly structured. Data Engineers play a crucial role in the data pipeline, building systems to acquire, transform, and load (ETL) data for analytics, reporting, and machine learning applications.
Design and Build Data Pipelines:
Develop, construct, test, and maintain data pipelines to extract, transform, and load (ETL) data from various sources to data warehouses or data lakes.
Ensure data pipelines are efficient, scalable, and maintainable, enabling seamless data flow for downstream analysis and modeling.
Work with stakeholders to identify data requirements and implement effective data processing solutions.
Data Integration:
Integrate data from multiple sources such as internal databases, external APIs, third-party vendors, and flat files.
Collaborate with business teams to understand data needs and ensure data is structured properly for reporting and analytics.
Build and optimize data ingestion systems to handle both real-time and batch data processing.
Data Storage and Management:
Design and manage data storage solutions (e.g., relational databases, NoSQL databases, data lakes, cloud storage) that support large-scale data processing.
Implement best practices for data security, backup, and disaster recovery, ensuring that data is safe, recoverable, and complies with relevant regulations.
Data Transformation:
Develop data transformation logic to clean, enrich, and standardize raw data, ensuring it is suitable for analysis.
Implement data transformation frameworks and tools, ensuring they work seamlessly across different data formats and sources.
Ensure the accuracy and integrity of data as it is processed and stored.
Automate repetitive tasks such as data extraction, transformation, and loading to improve pipeline efficiency.
Optimize data processing workflows for performance, reducing processing time and resource consumption.
Troubleshoot and resolve performance bottlenecks in data pipelines.
Collaboration with Data Teams:
Work closely with Data Scientists, Analysts, and business teams to understand data requirements and ensure the correct data is available and accessible.
Assist Data Scientists with preparing datasets for model training and deployment.
Provide technical expertise and support to ensure the integrity and consistency of data across all projects.
Data Quality Assurance:
Implement data validation checks to ensure data accuracy, completeness, and consistency throughout the pipeline.
Develop and enforce data quality standards to detect and resolve data issues before they affect analysis or reporting.
Monitor and improve data quality by identifying areas for improvement and implementing solutions.
Set up monitoring and logging for data pipelines to detect and alert for issues such as failures, data mismatches, or delays.
Perform regular maintenance of data pipelines and storage systems to ensure optimal performance.
Update and improve data systems as required, keeping up with evolving technology and business needs.
Document data pipeline designs, ETL processes, data schemas, and transformation logic for transparency and future reference.
Create reports on the performance and status of data pipelines, identifying areas of improvement or potential issues.
Provide guidance to other teams regarding the usage and structure of data systems.
Stay Updated with Technology Trends:
Continuously evaluate and adopt new tools, technologies, and best practices in data engineering and big data systems.
Participate in industry conferences, webinars, and training to stay current with emerging trends in data engineering and cloud computing.
Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, or a related field
Proficiency in programming languages such as Python, Java, or Scala for data processing.
Familiarity with data warehousing solutions (e.g., Apache NiFi, Talend, Informatica, Airflow).
Knowledge of big data technologies (e.g., Experience with cloud platforms (AWS, Azure, Google Cloud) and related services for data storage and processing.
Docker, Kubernetes) for building scalable data systems.
Understanding data modeling concepts (e.g., star schema, snowflake schema) and how they relate to data warehousing and analytics.
Knowledge of data lakes, data warehousing architecture, and how to design efficient and scalable storage solutions.
Strong problem-solving skills with an ability to troubleshoot complex data issues.
Strong attention to detail and a commitment to maintaining data accuracy and integrity.
Ability to work effectively in a collaborative, team-based environment.
3+ years of experience in data engineering, with hands-on experience in building and maintaining data pipelines and systems.
~ Proven track record of implementing data engineering solutions at scale, preferably in large or complex environments.
~ Experience working with data governance, compliance, and security protocols.
Experience with machine learning and preparing data for AI/ML model training.
Certification in cloud platforms (e.g., AWS Certified Big Data – Specialty, Google Cloud Professional Data Engineer).
Experience with DevOps practices and CI/CD pipelines for data systems.
Familiarity with data visualization and reporting tools (e.g., Tableau, Power BI) to support analytics teams
Opportunity to work with state-of-the-art technologies.
Supportive and dynamic team culture
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