Director, Data Engineering

Responsibilities:

• Data Engineering Strategy & Roadmap Own the data engineering roadmap, balancing new capability delivery with platform reliability, technical debt reduction, and cost optimization. Evaluate and recommend tooling, cloud services, and architectural patterns; build the business case for platform investments. Stay current with the Snowflake ecosystem and translate new capabilities (e.g., Openflow, Snowpipe, Cortex AI, Horizon, Streamlit, DBT, DAGs (or Airflow), etc.) into actionable roadmap items. Technical Responsibilities Own the design and ongoing hydration of the medallion architecture enabled by the Snowflake data platform, including ingestion patterns for the bronze (raw) layer, transformation contracts for the silver (conformed) layer, and aggregation/business logic standards for the gold (curated) layer. Establish and enforce coding standards leveraging the Data Build tool (dbt) for ELT data processing from model layering (staging → intermediate → mart), testing frameworks, etc. Design and maintain reusable dbt macros, packages, and custom generic tests to enhance engineering productivity and data quality. Manage dbt project governance, including environments (dev/qa/prod), dbt Cloud or CLI deployment pipelines, and dbt artifact storage. Drive adoption of dbt advanced features such as incremental strategies, snapshots, semantic layer, and exposure tracking. Lead the design and operation of the orchestration layer using; ensure DAGs are modular, idempotent, observable, and testable. Define standards for DAG development, dependency management, retry logic, SLA alerting, and backfill procedures. Integrate orchestration with dbt, ingestion tooling, and downstream consumers to deliver reliable end-to-end pipeline execution; enforce code review, branching strategy, and release management standards across the team. Establish data quality gates and automated testing at each tier boundary to prevent bad data from propagating downstream. Collaborate with domain teams to onboard new data sources into the bronze layer; enforce naming conventions, partition strategies, and retention policies. Manage platform costs through resource monitoring, auto-suspension policies, query optimization, and storage lifecycle management. Data Quality, Observability & Reliability Implement a data observability strategy using tooling such as Monte Carlo to detect anomalies, schema drift, and SLA breaches. Define and publish data SLOs for critical pipelines; lead incident response and post-mortems when SLOs are breached. Team Management & Development Define clear team OKRs aligned to organizational data strategy; hold the team accountable for delivery commitments and quality standards. Serve as the primary escalation point for technical blockers, cross-team dependencies, and platform incidents. Perform people management responsibilities; set performance objectives for direct reports, conduct performance reviews, train, coach, mentor, motivate, lead, and recruit new staff. Oversee all aspects of resource management (selection, training and development, performance management, retention, recognition) and maintain current staff information such as current headcount, budgeted headcount, turnover, staff salaries, incentive/merit information and staff organizational charts so that service disruptions are minimized.

Qualifications:

• Education/Experience Bachelor's degree or higher in Computer Science, Software Engineering, Information Systems, Mathematics, or a related technical discipline (or equivalent practical experience); 10+ years of hands-on data engineering experience (Informatica, Data Stage, python, dbt, SQL, etc.) 5+ years of people management experience leading data engineering teams; Demonstrated track record of delivering production Extract Load Transform (ELT) data integration capabilities at scale. Specific Knowledge & Skills (not preferred or an asset) Deep SQL expertise, data modeling techniques (Inmon, Kimbal, Vault, etc.), data quality or exception handling techniques, orchestration strategy, and performance tuning. Deep subject matter expert with cloud data platforms: Snowflake and/ or Databricks — Snowpipe, Openflow, RBAC, DBT, Snowpark, Horizon Catalog, Delta Lake, Unity Catalog, Spark, etc. ELT pipeline design & optimisation with dbt (data build tool) — models, tests, macros, packages; DAG orchestration (with airflow, prefect and/or dagster); Data warehouse and/or data lakehouse design using Medallion Architecture (bronze/silver/gold) design and hands-on experience with hydration; AWS, Azure and/or GCP native data services CI/CD for data (GitHub Actions, dbt Cloud, etc.); Data quality & observability tooling (Great Expectations, Monte Carlo, etc.); People management & technical leadership

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