Senior Data Engineer

  • Dallas, TX
  • Posted 2 days ago | Updated 2 days ago

Overview

Hybrid
$140,000 - $160,000
Full Time
No Travel Required

Skills

Hadoop
Pyspark

Job Details

Job Title: Senior/Lead Data Engineer
Location: Pittsburgh, PA/ Dallas, TX/ Cleveland, OH
Client: PNC Bank

Job Description:
PNC Bank is seeking a highly skilled Senior/Lead Data Engineer to join its Data & Analytics team. The ideal candidate will have strong expertise in building and optimizing scalable data pipelines using Hadoop and PySpark, with a focus on enabling data-driven decision-making across the enterprise. This role requires hands-on technical leadership, deep knowledge of big data technologies, and the ability to collaborate with cross-functional teams.

Key Responsibilities:

  • Design, build, and optimize large-scale data pipelines for structured and unstructured data.

  • Develop and maintain data ingestion, transformation, and integration workflows using PySpark, Hadoop (HDFS, Hive, HBase), and related ecosystem tools.

  • Implement best practices for data modeling, performance tuning, and pipeline optimization.

  • Collaborate with data scientists, analysts, and business stakeholders to deliver reliable and scalable data solutions.

  • Ensure data quality, security, and governance across data platforms.

  • Provide technical leadership and mentoring to junior data engineers.

  • Partner with cloud teams to integrate Hadoop/PySpark workloads with cloud platforms (AWS/Azure/Google Cloud Platform).

  • Troubleshoot, monitor, and optimize ETL workflows for high availability and performance.

  • Drive innovation by evaluating and recommending emerging technologies in the big data ecosystem.

Required Skills & Qualifications:

  • 10+ years of experience in Data Engineering, with at least 5+ years in Hadoop & PySpark.

  • Strong expertise in Hadoop ecosystem (HDFS, Hive, HBase, Oozie, Sqoop, Kafka, etc.).

  • Proficiency in PySpark, Python, and SQL for data transformation and analytics.

  • Hands-on experience with ETL/ELT processes, data modeling, and performance tuning.

  • Familiarity with data governance, lineage, and security best practices.

  • Experience integrating Hadoop with cloud platforms (AWS EMR, Azure Databricks, Google Cloud Platform Dataproc, etc.).

  • Strong understanding of distributed computing, parallel processing, and big data architecture.

  • Excellent problem-solving, communication, and leadership skills.

Preferred Qualifications:

  • Experience in the banking or financial services industry.

  • Exposure to streaming technologies (Kafka, Spark Streaming, Flink).

  • Familiarity with DevOps, CI/CD pipelines, and containerization (Docker/Kubernetes).

  • Knowledge of Snowflake or other modern data warehouses is a plus.

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