SENIOR TECHNICAL LEAD

Job Description Key Responsibilities Data Warehouse Modeling (End-to-End with DBT) This requires strong SQL skills to write complex queries to join multiple tables / views to extract the relevant data. Design and build end-to-end data warehouse layers using DBT, including Raw/staging → intermediate → marts, ensuring clean separation of concerns and reusable model patterns. Develop DBT models using strong SQL and sound modeling principles (e.g., star schema, SCDs, conformed dimensions, fact tables, auditability). Manage DBT model dependencies and orchestration design (sources, refs, exposures, selection syntax, tagging, and modular project structure). Implement DBT incremental models using appropriate strategies (e.g., merge/upsert patterns where required, rebuild/overwrite patterns when appropriate) with correctness and cost-efficiency. Use DBT pre-hooks and post-hooks for operational tasks such as auditing, schema/grant management, session configuration, cleanup, and metadata capture. Establish data quality with DBT tests (generic + custom), freshness checks, and controlled release processes. Snowflake Performance Tuning Optimization Tune SQL and Snowflake workloads for performance and cost, including: Query optimization (pruning, join strategy, aggregations, window functions, CTE usage) Efficient incremental processing and minimizing full-table scans Warehousing strategy (right-sizing compute, concurrency management, workload patterns) Table design decisions (clustering strategy where applicable, micro-partition awareness) Diagnose slow queries using query profiling techniques and implement improvements to reduce runtime and warehouse spend. Orchestration Production Operations (Airflow) Build and maintain Airflow DAGs for robust production orchestration, including: Parameterized deployments across environments Backfills, retries, SLAs, alerting, dependencies, and failure recovery patterns Clean DAG structure (TaskGroups, modular design; dynamic task patterns preferred) Coordinate DBT runs through Airflow with environment-aware configs, robust logging, and observability. Reliability, Governance, and Collaboration Ensure pipeline reliability through monitoring, alerting, idempotent design, and resilient recovery mechanisms. Apply strong data governance practices including access controls, role-based permissions, and handling of sensitive financial data. Collaborate with analysts, business stakeholders, and platform teams to translate complex requirements into well-modeled, trusted datasets. Required Qualifications 1. Expert SQL skills, including the ability to write production-grade transformations and create DBT models using advanced SQL (joins, window functions, complex aggregations, CTEs, performance-aware design). 2. Strong experience with dbt, specifically: a. Incremental models and strategy selection b. Pre-hooks and post-hooks c. Model dependency management and scalable project structuring d. Deployments across environments (dev/test/prod) and dbt execution patterns 3. Solid experience with Snowflake, including performance tuning and optimization (query performance, cost control, efficient loading patterns, warehouse sizing strategies). 4. Good hands on with Apache Airflow for orchestration and production operations. 5. Strong data warehousing fundamentals, including end-to-end warehouse design principles (dimensional modeling, SCDs, fact/dimension design, conformed dimensions, data lineage). 6. Proven analytical and problem-solving skills; ability to debug data issues across SQL/dbt/Snowflake/Airflow layers. 7. Experience working with large-scale, complex datasets in production environments. 8. Prefer candidates who have worked on Financial Services / Wealth Management projects.