Senior Data Engineer
New
N
NovistoESG data
Location: MontrealFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years of data engineering or backend engineering experience, with 2+ years in a senior or lead capacity.
- Required Skills
- PythonSQLSnowflakeAirflowData engineeringBigQuerydbtDatabricks
Requirements
- Have 6+ years of data engineering or backend engineering experience.
- Have 2+ years of experience in a senior or lead capacity.
- Hold a bachelor’s degree in Computer Science or equivalent.
- Have expert-level SQL and strong proficiency in Python.
- Have experience designing and operating a production lakehouse or cloud data warehouse such as Databricks, Snowflake, or BigQuery.
- Have experience with open table formats such as Delta, Iceberg, or Hudi.
- Have hands-on experience with dbt and orchestrators such as Airflow, Dagster, or Prefect.
- Have familiarity with cloud infrastructure, including Azure or GCP, CI/CD, containerization, infrastructure-as-code, and DevOps practices.
- Understand dimensional and analytical data modelling and batch and streaming/CDC ingestion patterns, including late-arriving data, schema drift, and backfills.
- Be able to integrate AI-assisted development tools into workflows and apply architectural patterns such as event-driven and service-oriented design.
Responsibilities
- Lead the design and evolution of the lakehouse, including storage layout, table formats, layering, partitioning, and retention.
- Model curated and semantic layers for analytics and reporting.
- Design batch, incremental, and CDC ingestion from source modules and establish data contracts.
- Build and operate ETL/ELT pipelines with dependency management, backfill strategies, and idempotent reprocessing.
- Build data quality tooling and platform observability, including lineage, SLAs, alerting, and cost telemetry.
- Own production health and incident response.
- Collaborate with engineering leads and product managers to align technical execution and consistency.
- Design backend services and APIs that expose curated datasets to product modules.
- Implement multi-tenant data isolation, authentication and authorization, and serving patterns such as query APIs, materialized views, and caching.
- Mentor developers and contribute to data engineering practices across squads.
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