Data Engineer
$120k - $135k per yearKindsight
About Kindsight:Kindsight builds technology that helps fundraisers make a difference. For decades, Kindsight has supported the education, healthcare, and nonprofit sectors with fundraising tools and the largest charitable giving database on the market. And as the giving sector evolves, so does Kindsight. As the leader in fundraising intelligence, Kindsight leverages real-time data and AI to help thousands of organizations around the world identify, manage, and engage with donors - at any scale. With purpose-built CRMs that corral all of that donor information and campaign tracking into one place, donor prospect research tools that offer proactive insights and real-time donor intel, and generative AI that creates personalized, meaningful content drafts at scale, Kindsight’s product suite is truly changing the game for donor fundraising.
Position Summary:We are seeking a hands-on Data Engineer to build and maintain the data platform behind Kindsight's customer-facing analytics products. You will work across the full pipeline: ingesting data from customer environments into our data lake, and transforming it through a dimensional data warehouse that powers dashboards and AI-driven insights. This is a strong fit for someone who enjoys building production data systems and wants depth in modern data engineering, cloud infrastructure, and multi-tenant analytics at scale.
What You’ll Do:
Build and maintain ELT pipelines that ingest data from customer Salesforce environments into our AWS data lake, covering both scheduled batch loads and near-real-time change data capture.
Develop data transformation layers (medallion architecture) and dimensional models in dbt on Redshift or Snowflake, implementing incremental logic, slowly changing dimensions, and derived metrics against defined specifications.
Write and optimize advanced SQL for transformation, aggregation, and data quality validation across large, multi-tenant datasets.
Work hands-on with AWS: S3, Glue, Athena, Redshift, Step Functions, Lambda, IAM, Lake Formation, and CloudWatch.
Onboard new customer data sources by extending existing configuration-driven pipelines, and verify each onboarding against defined acceptance criteria.
Build automated data quality checks, reconciliation queries, and monitoring so that pipeline failures are caught and diagnosed quickly.
Investigate and resolve data issues end to end — from source system behaviour through ingestion and transformation to the final reported number.
Contribute to infrastructure as code (AWS CDK or Terraform) for the pipelines and resources you work on.
Support dashboard and reporting development in QuickSight or Tableau, ensuring the underlying data models serve them correctly.
What We’re Looking For:
3+ years of hands-on data engineering experience building and operating production data pipelines.
Advanced SQL. You are comfortable with window functions, CTEs, complex joins, and query performance tuning, and you can debug a wrong number by reading the query plan and the data.
2+ years of hands-on AWS experience, including S3, Glue, Athena or Redshift, IAM, and at least one orchestration service such as Step Functions, or Airflow.
2+ years building ETL/ELT pipelines with dbt, or equivalent transformation frameworks, including incremental models and testing.
2+ years working with cloud data warehouses ;
Redshift, or Snowflake — including dimensional modeling with star schemas and query optimization.
Solid Python for data work: API integrations, data processing scripts, and pipeline code.
Experience with Git-based workflows, code review, and CI/CD for data pipelines.
Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
Bonus:
Experience integrating Salesforce data, including the REST and Bulk APIs, Change Data Capture, and Salesforce data models and objects.
Familiarity with fundraising or nonprofit sector data, or CRM and ERP data models more generally.
Experience with open table formats such as Apache Iceberg, or Delta Lake
Experience with multi-tenant data platforms and tenant isolation patterns.
Experience with infrastructure as code, particularly AWS CDK.
Experience with BI development in QuickSight or Tableau.
Compensation Range: $120,000 - $135,000 CAD OTE annually, based on experience, market benchmarks and role complexity. We aim to offer fair, competitive pay that reflects your skills and the market.
This advertised position is for an existing vacancy at Kindsight. At Kindsight, we’re proud to be a place where everyone belongs and has an equal opportunity to contribute, thrive and grow. We hire based on skills, potential, and impact, and we believe our differences fuel innovation. We welcome all individuals and do not discriminate on the basis of gender identity and expression, race, ethnicity, disability, sexual orientation, colour, religion, creed, gender, national origin, age, marital status, pregnancy, sex, citizenship, education, languages spoken or veteran status. We’re building a workplace where everyone has the opportunity to do meaningful work and make a difference.
We leverage artificial intelligence (AI) tools to support certain aspects of our recruitment process. These tools may help with resume screening, drafting job descriptions, creating interview questions and occasionally identifying potential candidates. All hiring decisions are made by our people, not AI. Our intent is to use AI thoughtfully to streamline administrative tasks, improve the candidate experience and support fair, unbiased hiring practices consistent with industry standards.
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Position Summary:We are seeking a hands-on Data Engineer to build and maintain the data platform behind Kindsight's customer-facing analytics products. You will work across the full pipeline: ingesting data from customer environments into our data lake, and transforming it through a dimensional data warehouse that powers dashboards and AI-driven insights. This is a strong fit for someone who enjoys building production data systems and wants depth in modern data engineering, cloud infrastructure, and multi-tenant analytics at scale.
What You’ll Do:
Build and maintain ELT pipelines that ingest data from customer Salesforce environments into our AWS data lake, covering both scheduled batch loads and near-real-time change data capture.
Develop data transformation layers (medallion architecture) and dimensional models in dbt on Redshift or Snowflake, implementing incremental logic, slowly changing dimensions, and derived metrics against defined specifications.
Write and optimize advanced SQL for transformation, aggregation, and data quality validation across large, multi-tenant datasets.
Work hands-on with AWS: S3, Glue, Athena, Redshift, Step Functions, Lambda, IAM, Lake Formation, and CloudWatch.
Onboard new customer data sources by extending existing configuration-driven pipelines, and verify each onboarding against defined acceptance criteria.
Build automated data quality checks, reconciliation queries, and monitoring so that pipeline failures are caught and diagnosed quickly.
Investigate and resolve data issues end to end — from source system behaviour through ingestion and transformation to the final reported number.
Contribute to infrastructure as code (AWS CDK or Terraform) for the pipelines and resources you work on.
Support dashboard and reporting development in QuickSight or Tableau, ensuring the underlying data models serve them correctly.
What We’re Looking For:
3+ years of hands-on data engineering experience building and operating production data pipelines.
Advanced SQL. You are comfortable with window functions, CTEs, complex joins, and query performance tuning, and you can debug a wrong number by reading the query plan and the data.
2+ years of hands-on AWS experience, including S3, Glue, Athena or Redshift, IAM, and at least one orchestration service such as Step Functions, or Airflow.
2+ years building ETL/ELT pipelines with dbt, or equivalent transformation frameworks, including incremental models and testing.
2+ years working with cloud data warehouses ;
Redshift, or Snowflake — including dimensional modeling with star schemas and query optimization.
Solid Python for data work: API integrations, data processing scripts, and pipeline code.
Experience with Git-based workflows, code review, and CI/CD for data pipelines.
Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
Bonus:
Experience integrating Salesforce data, including the REST and Bulk APIs, Change Data Capture, and Salesforce data models and objects.
Familiarity with fundraising or nonprofit sector data, or CRM and ERP data models more generally.
Experience with open table formats such as Apache Iceberg, or Delta Lake
Experience with multi-tenant data platforms and tenant isolation patterns.
Experience with infrastructure as code, particularly AWS CDK.
Experience with BI development in QuickSight or Tableau.
Compensation Range: $120,000 - $135,000 CAD OTE annually, based on experience, market benchmarks and role complexity. We aim to offer fair, competitive pay that reflects your skills and the market.
This advertised position is for an existing vacancy at Kindsight. At Kindsight, we’re proud to be a place where everyone belongs and has an equal opportunity to contribute, thrive and grow. We hire based on skills, potential, and impact, and we believe our differences fuel innovation. We welcome all individuals and do not discriminate on the basis of gender identity and expression, race, ethnicity, disability, sexual orientation, colour, religion, creed, gender, national origin, age, marital status, pregnancy, sex, citizenship, education, languages spoken or veteran status. We’re building a workplace where everyone has the opportunity to do meaningful work and make a difference.
We leverage artificial intelligence (AI) tools to support certain aspects of our recruitment process. These tools may help with resume screening, drafting job descriptions, creating interview questions and occasionally identifying potential candidates. All hiring decisions are made by our people, not AI. Our intent is to use AI thoughtfully to streamline administrative tasks, improve the candidate experience and support fair, unbiased hiring practices consistent with industry standards.
#J-18808-Ljbffr
Vacancy posted 1 hour ago
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