Big Data Engineer

EXL•Pittsburgh, PA

1d•Onsite

About The Position

We are looking for an experienced Big Data Engineer with 7–10 years of hands-on experience to design, build, and maintain scalable data pipelines and processing systems in an on-premises Big Data environment. Beyond strong individual contribution, the ideal candidate will own architecture and design decisions, set technical direction, and mentor and support other developers on the team. The role works closely with cross-functional teams to deliver reliable, high-quality data solutions that support business and analytics needs.

Requirements

7–10 years of overall experience in data engineering, with a proven track record in technical leadership (design ownership, mentoring, guiding development teams).
Python – strong hands-on development experience building production-grade data solutions.
Big Data / Hadoop ecosystem (Hadoop, Hive, Impala, HDFS) – deep, hands-on experience in on-premises environments.
Apache Spark – solid experience developing and tuning large-scale distributed data processing jobs.
Job scheduling / orchestration – hands-on experience with CA7 or Control-M (or comparable enterprise schedulers).
Strong understanding of data structures, ETL processes, and SQL.
Extensive experience with large-scale data processing and distributed systems.
Demonstrated ability to make sound architecture/design decisions and to mentor and support other developers.
Exposure to AI/ML concepts or tools, with a strong willingness to learn and grow in this space.

Responsibilities

Lead the design, development, and maintenance of robust, scalable data pipelines for ingestion, transformation, and processing of large datasets in an on-premises environment.
Own architectural and design decisions for data solutions, evaluating trade-offs and defining technical standards for the team.
Mentor, guide, and support other data engineers through code reviews, design reviews, technical coaching, and hands-on problem-solving.
Build and optimize data workflows using Python, Spark, and the Hadoop ecosystem.
Work extensively with Hadoop ecosystem components (Hive, HDFS, Impala) to manage and query large-scale data.
Manage and optimize batch scheduling and job orchestration using enterprise schedulers such as CA7 or Control-M.
Ensure data quality, integrity, and performance across data platforms.
Collaborate with data analysts, data scientists, and business stakeholders to translate data requirements into sound technical designs.
Troubleshoot and resolve complex issues in data pipelines and production environments, acting as an escalation point for the team.
Champion best practices for coding standards, version control, testing, and documentation.
Stay current with emerging technologies, particularly AI/ML capabilities, and identify opportunities to apply them to data engineering workflows.