Data Engineer

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: