Analytics Engineer, Meltwater
CA$95-110k
Media, social & consumer intelligence
Open for applications
Media, social & consumer intelligence
1001+ employees
Open for applications
CA$95-110k
1001+ employees
Company mission
To innovate, provide best-in-class service, and deliver sustainable growth through our technology and our people.
Role
Who you are
- 2-4 years of experience in data engineering, ETL development, and database management
- Proficiency in SQL and Python, with experience using ETL and reverse ETL tools like Fivetran, Census, Hightouch, and DBT
- Familiarity with data visualization tools (Tableau, Power BI, Looker, etc)
- Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies
- Strong problem-solving skills and attention to detail
- Effective communication skills to collaborate with cross-functional teams
- Preferred experience with data warehousing solutions such as Snowflake and Databricks
- Warehouse: Snowflake (Cortex AI, Snowpark, Snowpark Container Services, dynamic warehouses, RBAC)
- Transformation: dbt (dbt-snowflake)
- Orchestration: Apache Airflow 3.x on SPCS
- Languages: SQL, Python
- Tooling: SQLFluff (Snowflake dialect), Docker, Git, Fivetran (upstream ingestion)
- Integrations: Salesforce, Gong, Intercom, Google Ads, Zoom, Satismeter, Workday, Gainsight, Kantata, Saleshood, Jira, GCS
What the job involves
- Meltwater is seeking an experienced Analytics Engineer to join our Revenue Data & AI team and contribute to building a robust, AI-enriched data infrastructure that powers revenue-focused initiatives across Sales, Customer Success, Marketing, and Finance
- This role will report directly to a Sr AI Analytics Engineer, based in our Toronto office
- As an Analytics Engineer, you will play a key role in designing, building, and maintaining scalable data pipelines on Snowflake that transform raw data from CRM, sales engagement, support, and product systems into actionable insights
- You'll work hands-on with dbt, Apache Airflow, and Snowflake Cortex AI to deliver trusted datasets — from incremental staging models to semantic-search-ready marts
- You'll collaborate closely with cross-functional teams to understand data requirements and implement robust ELT processes, and you'll help extend our growing footprint of AI-powered models for call/email intelligence, sentiment, churn-risk signals, and revenue analytics
- Design, build, and maintain scalable dbt models on Snowflake/Databricks/Big Query that transform raw source data into reliable, well-documented datasets for analytics and reporting
- Build and orchestrate Airflow DAGs that coordinate dbt runs, Snowpark Container jobs, and external task sensors across 20+ source systems
- Integrate data from Salesforce, Gong, Intercom, Google Ads, Zoom, Satismeter, Workday, Gainsight, Kantata, Jira, and other external APIs into a unified revenue data model
- Develop incremental, merge-based models with appropriate unique keys, schema-change handling, and date-window filtering for cost-efficient refreshes
- Apply Snowflake Cortex AI functions (COMPLETE, SENTIMENT, TRANSLATE, SPLIT_TEXT_RECURSIVE_CHARACTER) to enrich text-heavy datasets such as call transcripts, emails, and survey responses, and produce vector-ready chunks for Cortex Search
- Collaborate on AI/ML pipelines that combine dbt transformations with Snowpark Container Services jobs (e.g., PII redaction) to produce safe, enriched 360° views of customer interactions
- Optimize warehouse usage, query patterns, and model materializations for performance, scalability, and cost — including query tagging for cost attribution and audit
- Establish and enforce data quality standards through dbt tests (uniqueness, not-null, referential integrity), source freshness checks, and Airflow short-circuit validators
- Implement data governance best practices — RBAC-driven schema promotion, masking/PII handling, lineage documentation, and compliant access patterns across source and prod
- Work closely with Revenue Operations, FP&A, Sales, CS, and Business Applications teams to translate business questions into well-structured datasets and semantic models
- Support the deployment of data-driven models and algorithms (AI tagging for use cases, churn-risk signals, competitor mentions, sentiment scoring, semantic search over conversations) in close collaboration with stakeholders
- Contribute to shared dbt macros, testing frameworks, and CI/CD practices (SQLFluff linting, Slack-based alerting)
Application process
- When You'll Join: August 2026
Our take
Meltwater's software analyzes and provides insights on posts across all major social media platforms, blogs, and news sites, allowing companies to make more informed marketing and operational decisions.
Launched in Norway in 2001 and later moving its headquarters to San Francisco, Meltwater has become a leading player in the media monitoring industry. It is an industry that is expected to double in value as the volume of data grows, making it increasingly challenging to derive meaningful insights.
Over the past two decades, Meltwater's growth has been fueled by strategic acquisitions of complementary businesses, enhancing its technology stack. Since going public in early 2021, and with major clients like Google, Ogilvy, The Economist, and Santander, Meltwater is poised to expand its operations beyond the 150 countries it currently serves.
Freddie
Company Specialist at Welcome to the Jungle
Insights
3% employee growth in 12 months
Company
Funding (2 rounds)
Dec 2023
$65m
GROWTH EQUITY VC
Mar 2019
$175m
GROWTH EQUITY VC
Total funding: $240m
Company HQ
Financial District, San Francisco, CA
Leadership
John Box
(CEO)A Meltwater veteran, they began as a Sales Manager in 2004. Climbed the ranks through roles as a Regional Managing Director, Global Sales Director, and EVP of Meltwater Social before becoming CEO in 2020.
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