Data engineer

22 hours ago

Kottayam, Kerala, India InCommon Full time

Experience: 3–6 yearsLocation: Remote (India)Engagement: Full-Time Employment / ContractorAbout the ProjectIn Common is hiring on behalf of Podifi to support a Fortune 500 client on a large-scale database migration initiative. The project involves transitioning from Fivetran to Nexla, with Snowflake as the target data platform. This role is critical for ensuring a smooth migration, maintaining data quality, and enabling scalable operations across multiple workloads. Timelines are aggressive, with immediate onboarding prioritized.Role OverviewWe are seeking hands-on Data Engineers with strong data migration experience and exposure to diverse database systems.Migrate ETL pipelines and plugins from Fivetran into Nexla/Snowflake.Build automation for ingestion, transformation, and observability.Deliver under tight deadlines in a fast-paced environment.This is a technical contributor role—ideal for someone who thrives on execution rather than high-level architecture.Key ResponsibilitiesExecute migrations from Fivetran and other legacy connectors into Snowflake/Nexla.Design and maintain ETL pipelines using Python, Py Spark, DBT, and related tools.Build and optimize API-based integrations (authentication, pagination, automation).Ensure data quality, consistency, and monitoring across pipelines.Contribute to prioritization and phased delivery of ~1,500+ plugins.Required Skills & Experience3–6 years in data engineering or ETL development, with hands-on migration work.Strong knowledge of Snowflake (data ingestion, transformation, optimization).Proficiency in Python for scripting and automation.Experience with Py Spark, DBT, and API-driven data integrations.Familiarity with AWS/cloud-hosted data solutions and observability.Solid track record in ETL → Data Engineering transition.Nice to HavePrior experience with large-scale plugin migrations.Familiarity with Fivetran, Nexla, or similar aggregation platforms.Ability to balance scripting-level problem-solving with broader data engineering.Comfort working in time-sensitive, high-stakes environments.

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