Job Description
Job Title:
Data Engineer 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 Containers & CI/CD:
Docker, Kubernetes Streaming tools: Kafka, Kinesis (advanced usage) Exposure to NLP and data segmentation Experience with data visualization tools Best Regards:
Tanuja P Phone:
Email: