Data Architect

Title: Data Architect 

Location: Fountain Valley, CA (100% Onsite )

Duration: 12 Months Contract

Job Description: Data Engineering Architect with 12+ years of experience in enterprise data engineering, ETL, data warehousing, cloud modernization and AI/ML solutions.

Technical Skills

• Strong expertise in Informatica PowerCenter, IDMC/IICS, PL/SQL, SQL, Python and Unix/Linux, with hands-on experience designing scalable ETL/ELT pipelines across heterogeneous sources including Oracle, SQL Server, PostgreSQL, DB2, MongoDB, APIs, JSON/XML and flat files.
• Experienced in AWS data and analytics services including S3, Glue, RDS, Redshift and Lambda, along with Databricks, Snowflake and Apache Airflow for cloud-based data engineering, orchestration and analytics architectures.
• Expertise in ETL architecture, complex transformations, full/incremental/CDC loads, SCD, data modelling, performance tuning, reconciliation and data quality.

Good Understanding of following:

• AI/ML capabilities covering Python, ML pipelines, MLOps, Generative AI, LLMs, RAG, embeddings, vector databases, LangChain/LangGraph and AI agents.
• Experience applying AI to data quality automation, anomaly detection, ETL/code generation, intelligent monitoring, RCA, data governance and enterprise data operations.
• Understanding of SDLC, architecture/design governance, CI/CD, Git, Agile delivery and technical leadership, with the ability to define enterprise data and AI architectures, lead solution design and mentor engineering teams.

 Roles & Responsibilities:

• Define end-to-end data architecture for ETL, data warehouse, lakehouse and cloud platforms.
• Design source-to-target mapping, complex transformations, full/incremental/CDC loads, SCD, data modelling, performance tuning, reconciliation and data quality.
• Lead Informatica PowerCenter/IDMC architecture, mapping design, workflows and ETL framework standards.
• Design scalable data pipelines using AWS S3, Glue, RDS, Redshift, Lambda, Databricks, Snowflake and Airflow.
• Develop and optimize PL/SQL, SQL and Python solutions for complex data processing and validation.
• Define data modelling, CDC, SCD, incremental loads, reconciliation and data quality frameworks.
• Architect AI/ML and GenAI solutions, including LLM, RAG, embeddings, vector databases and AI agents.
• Drive ETL modernization and cloud migration from legacy/on-prem platforms to cloud architectures.
• Establish performance, security, monitoring, CI/CD and data governance standards.
• Lead technical design reviews, provide architecture guidance, resolve complex technical issues and mentor engineering teams.
• Collaborate with business, data, cloud and AI teams to translate requirements into scalable, secure and cost-effective solutions.