Data Engineer

6 month Contract to Hire (Bangalore Preferred) Hyrbid/ Remote Required Skills & Experience Strong experience with big data technologies such as Apache Spark, Hadoop, and Hive. Hands‑on experience building batch data pipelines with a focus on performance, scalability, SLA adherence, and fault tolerance. Strong programming skills in Scala, with deep experience using Spark for data processing and analytics. Experience working with GCP services including BigQuery, Google Cloud Storage (GCS), Dataproc, and Pub/Sub. Solid experience writing and optimizing SQL, preferably BigQuery SQL and/or Spark SQL. Strong understanding of data modeling, ETL/ELT patterns, and data quality best practices. Experience with Kafka or similar messaging/streaming platforms. Familiarity with workflow orchestration tools (e.g., Airflow or Cloud Composer). Experience deploying and operating data pipelines in production cloud environments (GCP preferred, Azure acceptable). Strong troubleshooting skills and ability to optimize pipelines under real‑world constraints. Job Description We are seeking a skilled Data Engineer to design, build, and optimize large‑scale batch data pipelines in a cloud environment. This role focuses on reliability, performance, and data quality, supporting analytics and downstream consumers through well‑engineered big‑data solutions. The ideal candidate has strong experience with Apache Spark, cloud data platforms (GCP preferred), and writing performant SQL at scale. Key Responsibilities Design, develop, and maintain batch data pipelines using Apache Spark, Hadoop, Hive, or similar frameworks in a cloud environment. Build highly optimized, fault‑tolerant, and SLA‑driven data pipelines that operate reliably at scale. Leverage Google Cloud Platform (GCP) services such as BigQuery, GCS, Dataproc, and Pub/Sub to support data ingestion, processing, and storage. Write and optimize SQL queries (BigQuery SQL and/or Spark SQL) for data analysis, profiling, and performance tuning. Collaborate closely with analytics, data science, and downstream consumers to ensure data availability, correctness, and usability. Monitor and troubleshoot pipeline failures; implement alerting, retries, and data quality checks. Improve pipeline performance through partitioning, clustering, resource tuning, and query optimization. Follow software engineering best practices, including version control, testing, and documentation.