Key Responsibilities Data Analyst (Testing & Validation): 1. Perform end-to-end testing of ETL processes including data extraction, transformation, and loading across different data sources. Conduct data validation and reconciliation to ensure accuracy, completeness, and integrity of data across systems. Write and execute complex SQL queries to validate data quality and perform data analysis. Collaborate with developers, data engineers, and business analysts to understand data requirements and test cases. Design, document, and maintain test plans, test cases, and test scripts for data pipelines and BI reports. Identify and report data issues or anomalies , track defects, and verify resolutions through retesting. Use tools like Informatica, Talend, SSIS, or similar for ETL testing (based on tech stack). Ensure compliance with data governance, privacy, and quality standards during testing.
Data Pipeline Development: Build and maintain scalable ETL/ELT pipelines using Apache Beam, Dataflow, Cloud Data Fusion, or Composer (Airflow) . Data Warehousing: Design and optimize data models, schemas, and storage solutions in BigQuery . Data Integration: Work with structured and unstructured data from multiple sources using Pub/Sub, Cloud Storage, and Kafka . Data Governance & Security: Implement best practices for data quality, lineage, and access control following industry standards. Automation & Performance Optimization: Use Terraform for IaC, CI/CD pipelines, and monitoring tools like Stackdriver for performance tuning. Collaboration: Work with Data Scientists, Analysts, and DevOps teams to streamline workflows and ensure efficient data accessibility . Must-Have Skills: Google Cloud Services: BigQuery, Dataflow, Cloud Composer (Airflow), Cloud Storage, Cloud Functions, Pub/Sub Programming: Strong proficiency in Python, SQL (advanced queries, stored procedures, indexing, partitioning) ETL & Data Processing: Experience with Apache Beam, Spark, DataProc Infrastructure as Code: Terraform, Cloud Deployment Manager Data Security & Governance: IAM, VPC, encryption, GDPR compliance Nice-to-Have Skills: Experience with Kafka, Snowflake, Databricks Machine Learning Pipelines (AI/ML on GCP) Experience with Streaming Data Processing (Flink, Kinesis, Pub/Sub) DevOps/CI-CD (Jenkins, GitHub Actions, Cloud Build) Qualifications: Bachelors or Master’s in Computer Science, Data Engineering, or related field 3-8 years of experience in Data Engineering & GCP ecosystem GCP Certifications (Professional Data Engineer preferred)
Engage with US-based businesses through cold calling, emailing, and LinkedIn outreach to generate leads. Effectively pitch data science and analytics services, addressing client needs and pain points. Maintain accurate CRM records, manage follow-ups, and help convert leads into qualified meetings. Open to candidates with 04 years of experience – training and support will be provided for freshers. Must have excellent written and oral communication skills in English, with a willingness to work US time zones.
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