Data Infrastructure Specialist

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Weekday

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Fashion • Retail • eCommerce

Weekday is your everyday paradox wrapped in denim—unapologetic, unpredictable, and always evolving. Discover our full range of men’s and women’s fashion, from statement Weekday jeans and perfectly imperfect suiting to comfort-first underwear and bold pop culture-inspired graphic prints. Build your wardrobe with Astro jeans and chunky scuba pieces, ideal for creating relaxed yet modern outfits. Explore essential mini and maxi skirts, oversized shirts, short sleeve tops, and lightweight jackets—all available in muted tones and bold seasonal hues. Whether you're styling half-tucked tees or layered looks, Weekday offers statement pieces and versatile basics to fit your mood, your moment, and your everyday, embracing the many true expressions of youth.

501 - 1000 employees

Founded 2002

👗 Fashion

🛒 Retail

🛍️ eCommerce

📋 Description

• Architect, build, and optimize scalable ETL/ELT pipelines for large-scale structured and unstructured datasets using Spark, Databricks, Hive, or Glue. • Develop and maintain data integration workflows leveraging tools such as Informatica, Talend, or SSIS. • Write, optimize, and maintain complex SQL queries to support analytics and reporting. • Collaborate with cross-functional teams—including data scientists, analysts, and product stakeholders—to translate requirements into robust technical solutions. • Deploy and manage automated data workflows through CI/CD pipelines, ensuring smooth and reliable releases. • Monitor and enhance data pipeline performance, scalability, and reliability, proactively resolving bottlenecks. • Ensure data quality, governance, security, and regulatory compliance across all data workflows. • Work with cloud-based platforms such as Azure (ADF, Synapse, Databricks) or AWS (EMR, Glue, S3, Athena) to support distributed data processing. • Maintain clear and detailed documentation for data architectures, processes, and workflows. • Mentor junior team members, providing guidance on best practices, coding standards, and problem-solving approaches. • Stay up-to-date with emerging data technologies, tools, and industry best practices to continuously improve data infrastructure.

🎯 Requirements

• Bachelor’s degree in Computer Science, IT, or a related technical discipline. • 4+ years of hands-on experience in designing and implementing data pipelines and distributed data processing solutions. • Strong expertise with Databricks, Spark, or equivalent technologies (EMR, Hadoop). • Proficiency in Python or Scala for data processing and transformation tasks. • Experience with modern data warehouses such as Snowflake, Redshift, or Oracle. • Solid understanding of distributed storage systems (HDFS, ADLS, S3) and data formats like Parquet and ORC. • Familiarity with orchestration and workflow management tools like ADF, Airflow, or Step Functions. • Databricks Data Engineering Professional certification is a plus. • Exposure to multi-cloud environments or cloud migration projects is advantageous.