Data Engineer

One of our US Based client who is transforming how people buy and sell secondhand apparel, and data is central to that mission. We're looking for a Data Engineer to join our Data Engineering team with a focus on analytics engineering and enabling scalable, self-service data across the organization.

In this role, you'll design, build, and maintain scalable data infrastructure and robust pipelines to support a wide range of analytics use cases. You'll collaborate with cross-functional partners to develop trusted data models that power reporting, analytics, and automation. You'll also contribute to platform-level efforts around content governance and performance optimization. This is a foundational role for someone who is eager to deepen their cloud-native data engineering skills, explore the application of AI in modern data workflows and drive business impact.

What you will do

? Design, implement, and maintain scalable data and analytics infrastructure to support diverse datasets, ensuring high availability, performance, and data integrity.

? Build and optimize robust ELT pipelines using dbt and Databricks to ingest and transform structured and semi-structured data across business domains.

? Develop accessible and trusted data models to enable self-service analytics and operational reporting.

? Collaborate with analysts, data scientists, and product managers to understand stakeholder needs and deliver actionable insights.

? Contribute to platform usability and governance through workspace organization, access management, and performance tuning.

? Participate in debugging, monitoring, and ensuring data quality across production pipelines.

? Stay curious about emerging technologies, with an emphasis on incorporating AI capabilities and automation into workflows.

Qualification

?4+ yrs in data/analytics engineering, strong SQL + Python/Spark, hands-on with Databricks & dbt

? Strong development expertise in Python with experience building and deploying robust

services, adhering to software engineering principles.

? Proficiency in SQL and data modeling for analytics use cases, with a strong

understanding of data quality and semantic principles.

? Experience building distributed data pipelines using Spark, Databricks, or cloud-native

data tools.

? Familiarity with AI/ML workflows and an eagerness to explore their application in

production environments, particularly regarding feature engineering and AI

experimentation.

? Proficiency in building data pipelines using Airflow and Apache Spark (RDD/DataFrames/Structured Streaming)

? Experience with distributed messaging systems like Kafka, SQS, or RabbitMQ.

? Knowledge of big data lake architectures using S3, Parquet/Avro, and Delta Lake in Databricks.

? Strong hands-on experience with schema validation (e.g., Pydantic, Avro) and data QA practices ? Comfort with Docker, Kubernetes/ECS, and production deployment workflows.

? Working knowledge of CI/CD, infrastructure-as-code, and monitoring tools like Datadog.

? Familiarity with building REST/gRPC APIs and data-serving interfaces.

? Ability to write scalable, reusable, and testable code with a focus on performance, cost, and reliability.

? Understanding of CI/CD tooling, structured testing frameworks, and system-level

monitoring.

? Excellent problem-solving and collaboration skills across engineering, product, and

analytics teams.

What you'll own