Sr AI Engineer

About LatentView Analytics

Founded in 2006, LatentView Analytics began with a shared passion for the world of data. Today, over 20 years on, we've grown into a close-knit community of people united by that same drive solving real business challenges with data and AI. We work with industry leaders worldwide, specializing in end-to-end analytics that goes beyond the buzzwords to deliver genuine business impact. Our focus has stayed consistent since day one: helping clients derive meaningful insights and drive growth through a thoughtful, sustainable approach to data analytics and AI.

Role Overview

We are hiring Data Engineers to work across pipeline development, cloud data platforms, data modeling, and orchestration functions supporting our clients. You will design and build the data infrastructure that powers analytics, reporting, and AI/ML initiatives, working closely with Data Architects, Data Scientists, and business stakeholders. The specific focus of your role cloud platform, specialization, and scope of ownership will be matched to your experience and skills.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines to ingest, transform, and load data from diverse sources
• Write and optimize advanced SQL queries for data extraction, transformation, validation, and reporting
• Build and maintain data models, warehouses, and semantic layers to support analytics, reporting, and downstream consumption
• Work with cloud data platforms and services to enhance data processing, storage, and scalability
• Automate and orchestrate end-to-end data workflows using tools such as Airflow, dbt, Cloud Composer, or Data Factory
• Collaborate with cross-functional stakeholders Data Architects, Data Scientists, Analysts, and business teams to translate requirements into technical solutions
• Perform data validation, reconciliation, and quality checks to ensure accuracy, consistency, and governance across pipelines
• Document data pipelines, schemas, and technical processes to support knowledge sharing and maintainability
• Troubleshoot pipeline failures, performance bottlenecks, and data quality anomalies
• Mentor junior engineers and contribute to best practices, code reviews, and continuous improvement (scope matched to seniority)

Skills Experience

• 410 years of relevant experience in Data Engineering, Data Pipeline Development, or a related field
• Advanced SQL complex joins, CTEs, window functions, and query performance optimization
• Hands-on proficiency in Python and/or PySpark for data transformation, automation, and processing
• Proven experience building and maintaining ETL/ELT pipelines end-to-end
• Hands-on expertise in at least one cloud data platform (AWS, GCP, or Azure) and its associated warehouse/lake service (Snowflake, BigQuery, Databricks, or Microsoft Fabric)
• Experience with workflow orchestration tools (Airflow, dbt, Cloud Composer, Data Factory, or similar)
• Strong problem-solving skills and the ability to collaborate effectively with technical and non-technical stakeholders

Good to Have(any one)

• Marketing/Media Data Engineering MLOps: Data mart and harmonization design across disparate sources, MLOps and model deployment, model monitoring, cost optimization
• Unstructured Data GenAI-Adjacent Engineering (GCP): Document AI, Vertex AI, embeddings and vector search, semantic data modeling, Agentic AI/LangChain exposure
• Cloud-Native Pipeline Engineering (AWS): S3, Lambda, SNS, Step Functions, NumPy/Pandas, independent end-to-end ownership as an individual contributor
• Databricks DataOps BI Enablement: Databricks and PySpark at scale, data validation and reconciliation, BI tool exposure (Power BI, Tableau, or Looker), Git/CI-CD discipline
• Microsoft Fabric Platform Engineering: Data Factory/Dataflows Gen2, Medallion architecture (Bronze/Silver/Gold, Delta Lake, OneLake), Power BI DAX/Direct Lake, real-time streams (Eventstreams/KQL), Purview governance
• Team Delivery Leadership: Mentoring, code reviews, sprint/delivery ownership, cross-functional and business stakeholder management

Qualifications

• Bachelor's or Master's degree in a quantitative field (Computer Science, Engineering, or related) or equivalent practical experience
• Strong analytical, problem-solving, and communication skills

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.