Technical Architect - Google Cloud Platform (15-17 yrs)

Job Summary :

We are seeking a highly skilled Data Engineering Technical Architect with deep expertise in Google Cloud Platform (GCP), Apache Spark, and SAP S/4HANA (Data ingestion) to architect, design and implement scalable, high-performance data lake solutions. The ideal candidate will have extensive experience in building data ingestion pipelines, managing big data processing using Apache Spark, and integrating SAP S/4HANA with GCP cloud infrastructure.

Key Requirements :

- Over 10 years of professional experience in data engineering, specializing in implementing large-scale enterprise Data Engineering projects with the latest technologies.

- Over 5 years of hands-on experience in GCP technologies and over 3 years of lead experience.

- Design and implement end-to-end data architectures leveraging GCP services (e.g., Big Query, Cloud Storage, Dataflow, Pub/Sub, Cloud Composer) for large-scale data ingestion and processing.

- Build and optimize large-scale data pipelines using Apache Spark on GCP (via Dataproc or other Spark services). Ensure high performance and scalability in Spark-based data processing workloads.

- Lead the integration of SAP S/4HANA data with GCP for real-time and batch data processing. Manage data extraction, transformation, and loading (ETL) processes from SAP S/4HANA into cloud storage and data lakes.

- Develop and manage scalable data ingestion pipelines for structured and unstructured data using tools like Cloud Dataflow, Cloud Pub/Sub, and Apache Spark.

- Provide architectural guidance for designing secure, scalable, and efficient data solutions on the Google Cloud Platform, integrating with on-premise/cloud systems like SAP S/4HANA.

- Implement both real-time streaming and batch processing pipelines using Apache Spark, Dataflow, and other GCP services to meet business requirements.

- Implement data governance, access controls, and security best practices to ensure the integrity, confidentiality, and compliance of data across systems.

- Collaborate with business stakeholders, data scientists, and engineering teams to define data requirements, ensuring the architecture aligns with business goals.

- Optimize Apache Spark jobs for performance, scalability, and cost-efficiency, ensuring that the architecture can handle growing data volumes.

- Provide technical leadership to the data engineering team, mentoring junior engineers in data architecture, Apache Spark development, and GCP best practices.

Technical Expertise :

- Expert-level programming proficiency in Python, Java, and Scala.

- Extensive hands-on experience with big data technologies, including Spark, Hadoop, Hive, Yarn, MapReduce, Pig, Kafka, and PySpark.

- Proficient in Google Cloud Platform services, such as BigQuery, Dataflow, Cloud Storage, Dataproc, and Cloud Composer Google Pub/Sub, and Google Cloud Functions.

- Expertise in Apache Spark for both batch and real-time processing, as well as proficiency in Apache Beam, Hadoop, or other big data frameworks.

- Experienced in using Cloud SQL, BigQuery, and Looker Studio (Google Data Studio) for cloud-based data solutions.

- Skilled in orchestration and deployment tools like Cloud Composer, Airflow, and Jenkins for continuous integration and deployment (CI/CD).

- Expertise in designing and developing integration solutions involving Hadoop/HDFS, real-time systems, data warehouses, and analytics solutions.

- Experience with DevOps practices, including version control (Git), CI/CD pipelines, and infrastructure-as-code (e.g., Terraform, Cloud Deployment Manager).

- Strong background in working with relational databases, NoSQL databases, and in-memory databases.

- Experience managing large datasets within Data Lake and Data Fabric architectures.

- Strong knowledge of security best practices, IAM, encryption mechanisms, and compliance frameworks (GDPR, HIPAA) within GCP environments.

- Experience in implementing data governance, data lineage, and data quality frameworks.

- In-depth knowledge of web technologies, application programming languages, OLTP/OLAP technologies, data strategy disciplines, relational databases, data warehouse development, and big data solutions.

- Led end-to-end processes for the design, development, deployment, and maintenance of data engineering projects.

- Excellent debugging and problem-solving skills.

- Retail and e-commerce domain knowledge is a plus.

- Positive attitude with strong analytical skills and the ability to guide teams effectively.

Preferred Qualifications :

- GCP Certifications: Such as Professional Data Engineer or Professional Cloud Architect.

- Apache Spark and Python certifications.

- Experience with Data visualization tools like Tableau, Power BI etc.