We are looking for a Senior/Lead Data Engineer to join our Data practice and take ownership of designing, building, and operating cloud data platforms for enterprise clients and existing intelligence products. You will establish standards for data pipelines and modeling, review engineering work, and make architectural decisions in collaboration with Data Science, Strategy, and Technology teams.

You will also design the data foundations and agentic workflows that AI agents rely on, ensuring data is governed, well-modeled, retrievable with low latency, and safe for agents to access and act upon.

Key Responsibilities

• Architect and deliver batch and streaming data pipelines across Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Cloud Composer), AWS (Redshift, S3, Glue), and Snowflake for Fortune 500 clients.

• Design dimensional and domain-specific data models using dbt, and make them accessible through semantic layers that analysts, applications, and AI agents can query reliably.

• Develop data products for agentic systems, including retrieval layers, embedding pipelines, vector stores, and governed tool interfaces such as APIs and MCP servers used by AI agents.

• Design agentic data workflows that enable agents to monitor pipelines, triage failures, generate tests and documentation, and propose fixes for engineer review.

• Collaborate with cross-functional teams to use agentic workflows to accelerate pipeline development, testing, and migration from legacy systems, validating every change before deployment.

• Establish and enforce data engineering standards for orchestration, CI/CD, infrastructure as code, testing, and observability, including evaluation mechanisms and guardrails for agent-driven changes. Ensure compliance through code reviews.

• Take ownership of data quality, lineage, security, and cost management across platforms, including access controls and audit trails for agent access to data.

• Mentor Data Engineers, assign and review tasks, and represent the data engineering team in technical discussions.

• Translate client business requirements into technical designs and estimates for new engagements.

Requirements

• At least 3 years of professional Data Engineering experience delivering production data pipelines for business or enterprise clients.

• Demonstrated ownership of at least one production data platform or major pipeline domain, including on-call duties or incident management responsibilities.

• Advanced SQL skills and strong Python proficiency for data processing and automation.

• Hands-on experience with cloud data warehouses, with deep expertise in at least one of Snowflake, AWS Redshift, or BigQuery, and working experience with a second platform.

• Production experience with dbt for data transformation and modeling, as well as a BI or semantic layer tool such as Sigma, Looker, or Tableau.

• Experience with orchestration tools such as Airflow, Cloud Composer, or Dagster, and cloud data services on GCP or AWS, including Dataflow, Glue, S3, Pub/Sub, or Kinesis.

• Experience in data modeling, data warehouse design, and performance and cost optimization.

• Working knowledge of how data supports agentic AI applications, including embeddings, vector search, retrieval-augmented generation (RAG), tool calling, agent frameworks, and MCP.

• Daily experience using AI coding agents such as Claude or Gemini, or other LLM-based tools, in engineering workflows, with the ability to review and validate their outputs.

• Experience with CI/CD tools such as GitHub Actions or similar, Terraform or equivalent infrastructure-as-code tools, and automated data testing.

• Experience leading or mentoring other engineers, either formally or informally.

• A Bachelor's degree in Computer Science, Engineering, or equivalent professional experience.

• Advanced English proficiency for collaboration with global teams.