Ebay MTS Software Engineer Data 

India, Karnataka, Bengaluru 

846388472

• Designing & Building Data Pipelines: Creating systems (often called ETL or ELT pipelines) that automatically extract data from various sources (like databases, APIs, and log files), transform it into a usable format, and load it into a central repository.

• Managing and optimizing large-scale data storage solutions, such as data warehouses (for structured data) and data lakes (for raw, unstructured data).

• Support platform capabilities across ingestion, streaming, lakehouse/warehouse, catalog, and governance .

• Data Modeling: Designing the structure of data to make it efficient for analysts and data scientists to query..

• Ensuring Data Quality & Governance: Implementing checks and processes to ensure data is accurate, consistent, reliable, and secure.

• Optimizing Performance: Tuning data pipelines and database queries to ensure they run quickly and efficiently, even with massive volumes of data.

• Collaboration:

• 8+ years of professional software engineering experience (or equivalent impact)..

• Expert-Level SQL: You must be able to write complex, efficient SQL queries to retrieve, manipulate, and transform data.

• Strong coding skills in Java/Python and familiarity with CI/CD .

• Hands-on with some of: Kafka/Flink , Spark , Delta/Iceberg , Kubernetes , NoSQL/columnar stores .

• Proven ability to work independently , make sound tradeoffs, and deliver quality outcomes with minimal supervision.

• Solid debugging, performance analysis, and system design skills.
  

• Relational Databases (SQL): (e.g., PostgreSQL, MySQL)

• NoSQL Databases: (e.g., MongoDB, Cassandra) for unstructured data.

Data Pipeline & Orchestration Tools:

• Apache Airflow: The industry standard for scheduling, automating, and monitoring data pipelines.

Qualifications

• Education: A Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or a related technical field. A Master's degree is a plus.

• Impact at scale: Your platform work powers analytics and ML across a global marketplace.

• Hard problems: Streaming freshness/correctness, storage/compute efficiency, multi-region resiliency.

• Collaborative culture: