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Tier-1 brand, metro location, and mid-level generalist data role drive high competition.
Data engineering and cloud/tooling skills are broadly transferable across industries.
Explicit 3+ years requirement plus mandatory tools (Spark, SQL, Airflow) enforces strict filters.
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Develop and maintain automated data pipelines to ingest, transform, and deliver analytics datasets for multiple Salesforce functions.
Collaborate with senior engineers and business partners to implement data transformations, models, and analytics-friendly schemas supporting reporting and analysis.
Support data quality, validation, monitoring, and participate in ad-hoc analysis; contribute to improving data reliability and documentation while adopting best practices in data engineering.
3+ years experience in building, implementing, and maintaining data warehousing and analytics solutions.
Hands-on experience with distributed data processing frameworks such as Spark, Hive, or Iceberg and proficiency in SQL and one or more programming languages such as Python, Java, or Scala.
Experience with data orchestration tools like Airflow, MPP analytical databases (Snowflake, Redshift), and cloud platforms preferably AWS.
Work Experience Required: Minimum 3+ years as explicitly mentioned. Notice period: Not explicitly mentioned in the JD.
Experienced in developing scalable data infrastructure to support cross-functional analytics and business intelligence needs in a large enterprise environment.
Familiar with integrating AI agents or LLM-powered workflows into data engineering processes to improve automation and productivity.
Comfortable collaborating with diverse stakeholders including data analysts, scientists, and product teams to deliver impactful data-driven solutions aligned with organizational goals.