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About
Location: Dallas, TX, Oklahoma City, Ok, Little Rock, AK, Shreveport, LI, Albuquerque, NM
Looking for W2 No C2C
Job Description
Lead the design, development, and optimization of large-scale, secure, and high-performance data pipelines across batch, real-time, and event-driven systems.
Partner with cross-functional teams to deliver analytics, reporting, and ML-ready datasets.
Key Responsibilities
Architect, build, and optimize batch and real-time data pipelines for enterprise-scale systems.
Integrate datasets from databases, files, APIs, and event streams (Kafka/Kinesis).
Ensure data quality, scalability, reliability, and performance across pipelines.
Prepare curated datasets for analytics, reporting, and ML models.
Implement data security (access control, encryption, masking) and governance practices.
Monitor, troubleshoot, and tune data infrastructure for optimal efficiency.
Collaborate closely with data scientists, architects, and business teams to define data solutions.
Introduce best practices, mentor engineers, and drive data engineering standards.
Mandatory Skills (with Experience)
Data Engineering (Overall): 8-12 years
Python / Java / Spark: 6-10 years (strong coding)
SQL & NoSQL Databases: 6-10 years (RDS, Redshift, DynamoDB, Synapse, BigQuery, MongoDB)
Batch & Streaming Systems: 5-8 years (Kafka/Kinesis/event-driven systems)
Cloud Experience: 5-8 years across AWS, Azure, Google Cloud Platform (minimum two clouds)
APIs & Messaging Systems: 4-6 years (REST, event-driven architectures)
Large-scale Dataset Engineering: 5-8 years (performance optimization)
Graph Databases: 1-2 years (Neptune, RDF4j)
Vector Databases: 1-2 years (Pinecone, FAISS)
Data Security: 3-5 years (encryption, access control, masking)
Soft Skills
Strong problem-solving & analytical thinking
Excellent communication across technical & business teams
High ownership, adaptability, and collaborative mindset
Nice to Have
Domain knowledge: Insurance, Banking, Mortgage
Streaming tools: Kafka, Kinesis (advanced usage)
Exposure to NLP and data segmentation
Experience with data visualization tools
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Languages
- English
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