Lead Data Engineer + AI Client – Altimetrik Takeda Location: Remote Need minimum 3 years of experien

Lead Data Engineer + AI ClientAltimetrik Takeda Location: Remote Need minimum 3 years of experience as Lead.About the role We're looking for a Senior Data Engineer to build and scale our Lakehouse and AI data pipelines on Databricks. You'll design robust ETL/ELT, enable feature engineering for ML/LLM use cases, and drive best practices for reliability, performance, and cost.What You'll DoDesign, build, and maintain batch/streaming pipelines in Python + PySpark on Databricks (Delta Lake, Autoloader, Structured Streaming).Implement data models (Bronze/Silver/Gold), optimize with partitioning, Z-ORDER, and indexing, and manage reliability (DLT/Jobs, monitoring, alerting).Enable ML/AI: feature engineering, MLflow experiment tracking, model registries, and model/feature serving; support RAG pipelines (embeddings, vector stores).Establish data quality checks (e.g., Great Expectations), lineage, and governance (Unity Catalog, RBAC).Collaborate with Data Science/ML and Product to productionize models and AI workflows; champion CI/CD and IaC.Troubleshoot performance and cost issues; mentor engineers and set coding standards.Must-Have Qualifications10+ years in data engineering with a track record of production pipelines.Expert in Python and PySpark (UDFs, Window functions, Spark SQL, Catalyst basics).Deep hands-on Databricks: Delta Lake, Jobs/Workflows, Structured Streaming, SQL Warehouses; practical tuning and cost optimization.Strong SQL and data modeling (dimensional, medallion, CDC).ML/AI enablement experience: MLflow, feature stores, model deployment/monitoring; familiarity with LLM workflows (embeddings, vectorization, prompt/response logging).Cloud proficiency on AWS/Azure/GCP (object storage, IAM, networking).CI/CD (GitHub/GitLab/Azure DevOps), testing (pytest), and observability (logs/metrics).Nice to HaveDatabricks Delta Live Tables, Unity Catalog automation, Model Serving.Orchestration (Airflow/Databricks Workflows), messaging (Kafka/Kinesis/Event Hubs).Data quality & lineage tools (Great Expectations, OpenLineage).Vector DBs (FAISS, pgvector, Pinecone), RAG frameworks (LangChain/LlamaIndex).IaC (Terraform), security/compliance (PII handling, data masking).Experience interfacing with BI tools (Power BI, Tableau, Databricks SQL).