Data Engineering

Ampcus Inc
New York, United States of America
yesterday

Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

New York, United States of America

Tech stack

Java
Azure
Big Data
Information Engineering
ETL
Data Transformation
Data Structures
Database Queries
Software Debugging
Distributed Computing Environment
Hadoop
Python
Software Engineering
SQL Databases
Data Processing
Snowflake
Spark
PySpark
Amazon Web Services (AWS)
Databricks

Job description

  • This is a hands-on senior engineering role, not an associate-level or purely architectural position.
  • Approximately 60% data processing/data ecosystem work and 40% general programming, potentially in Python or Java.
  • Python fundamentals matter beyond PySpark syntax. Candidates may be questioned on language internals, programming fundamentals, data structures, and algorithms.
  • Spark is foundational. AWS EMR and Glue ETL are highly relevant, but strong Spark candidates from Azure, Databricks, Snowflake, Hadoop, or comparable ecosystems may be considered.
  • The team expects end-to-end data thinking: modeling, wrangling, pipelines, storage, quality, security, monitoring, and operational resilience.
  • Candidates must collaborate, coach, document decisions, and explain how their work contributed to a team outcome.
  • Rsum claims will be tested. The interview may include live Python coding and technical deep dives.

Requirements

  • Strong Python programming fundamentals and hands-on coding ability (Java may complement, but not replace, the requested Python depth).
  • Strong Apache Spark / PySpark fundamentals, including distributed processing and performance considerations.
  • 5 10+ years of relevant data engineering or big-data processing experience, with senior/lead-level scope.
  • Practical data engineering across pipelines, modeling, warehouses/lakes, migration, quality, storage, access, and analytics.
  • Working knowledge of data structures and algorithms; able to reason through lists, stacks, queues, complexity, and solution design.
  • System design, application development, testing, debugging, operational stability, and SQL/database querying.
  • Clear, credible communication: explains when, where, why, and how each key rsum skill was used.
  • Teamwork and technical leadership: collaboration, coaching, documentation, and ownership.

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