Senior Data Engineer

  • Pittsburgh, PA
  • Posted 2 days ago | Updated 2 days ago

Overview

Hybrid
110000 - 130000
Contract - W2
Contract - 6 Month(s)
10% Travel
Unable to Provide Sponsorship

Skills

Analytics
Apache Hadoop
Apache Hive
Apache Spark
Continuous Delivery
Data Engineering
Data Governance
Data Modeling
Data Quality
HDFS
Extract, Transform, Load
Git
PySpark
Python
Snow Flake Schema
Streaming
Version Control
Workflow
Orchestration
Kubernetes
CI/CD
Meta Model

Job Details

<>Job Title: Data Engineer – Pipeline Focus (Contract-to-Hire)
Location: Hybrid / Remote - Pittsburgh, PA / Dallas, TX / Cleveland, OH
Industry: Financial Services
Type: Contract to Hire (3-6 Months)
Sponsorship: Not Available</>

Join a fast-growing data engineering team at a leading financial institution undergoing a major data modernization initiative. We’re looking for hands-on Data Engineers with strong pipeline and data operations experience to support scalable, secure, and analytics-ready data platforms across commercial, retail, and wealth domains.

You’ll work on high-impact initiatives spanning both on-prem as well as preparing for future state cloud deploytments, with direct involvement in ingestion, transformation, quality, and governance. Ideal for engineers who enjoy building data infrastructure and solving real-world data problems with code.

Key Responsibilities:

  • Design, build, and optimize batch and streaming data pipelines using Python, PySpark, and SQL

  • Work with distributed compute (e.g., Hadoop, Spark) and HDFS formats

  • Collaborate with data analysts, scientists, and product teams to transform raw data into model-ready datasets

  • Monitor, test, and ensure data quality across pipelines using custom validation logic

  • Deploy pipelines and workflows using CI/CD, Git, and version control best practices

  • Participate in cloud-native data engineering across AWS, Azure, or Google Cloud Platform

  • Ensure compliance with data governance and security standards

Required Skills:

  • Strong programming experience with Python, PySpark, SQL

  • Hands-on experience with Hadoop, Hive, Spark, and HDFS

  • Familiarity with data modeling, schema management, and ETL/ELT workflows

  • Cloud experience with AWS, Azure, or Google Cloud Platform

  • Experience with version control (Git) and CI/CD pipelines

  • Understanding of data quality, governance, and observability tools

Bonus: Experience with modern data orchestration tools (Airflow, dbt), Snowflake, or data catalog tools like Collibra or Alation.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

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