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Ambyint

AI-driven well optimization software and hardware

Head of Data Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeCalgary, AB, CanadaNo salary listed

About the Job

Requirements

  • A bachelor's or master's degree in Computer Science, Engineering, or a related quantitative field.
  • At least 8 years of progressive experience in data engineering, including at least 3 years in a leadership, managerial, or staff/principal architectural role.
  • A proven track record of designing, deploying, and scaling enterprise data platforms that handle high-volume IoT, streaming, and batch data.
  • Deep technical expertise in cloud data warehouses such as Snowflake, Databricks, or BigQuery; ETL platforms such as Azure data pipelines, Fivetran, or Stitch; and orchestration and transformation tools such as dbt, Airflow, or Dagster.
  • A strong background in financial management for cloud resources, compute unit cost optimization, and FinOps best practices.
  • Expertise in establishing DataOps processes, continuous integration and deployment frameworks, unit testing paradigms, and SOC2 and data governance policies.
  • Demonstrated ability to build, manage, and scale high-performing engineering teams through effective hiring, coaching, and retention.
  • Executive-level communication and stakeholder management skills, including translating complex technical concepts into strategic business outcomes.
  • Experience managing end-to-end Business Intelligence platforms, data modeling for analytics, and modern visualization tools such as Tableau, Power BI, or Looker.

Responsibilities

  • Lead the vision, strategy, and execution of enterprise data architecture and roadmap.
  • Manage and empower a team of data engineers and data analysts, setting technical standards for the artificial lift optimization platform.
  • Shape long-term data strategy by balancing scalability, security, cost efficiency, and platform performance.
  • Work with executive leadership, product managers, data science leaders, and software engineering heads to turn industrial data streams into scalable, reliable data products.
  • Set the strategic vision and multi-year roadmap for enterprise data architecture, platform scaling, analytics, and streaming and batch capabilities.
  • Lead, mentor, and recruit the data engineering team, driving career development and team performance.
  • Partner with executive leadership, Data Science, and Product to align data engineering priorities with business goals.
  • Drive analytics and business intelligence strategy by delivering self-service reporting capabilities, executive dashboards, and enterprise data models.
  • Establish enterprise DataOps standards, continuous integration and deployment pipelines, and engineering practices.
  • Define and enforce data governance, quality frameworks, lineage, and metadata management across data assets.
  • Own SOC2 compliance, data privacy, and security protocols across data stores and cloud infrastructure.
  • Establish governance, semantic consistency, and quality controls across BI platforms and reporting models.
  • Manage the data platform budget and optimize cloud warehouse consumption and vendor tooling costs.
  • Oversee cross-functional delivery schedules, resource allocation, and technical debt management across pipelines and platform infrastructure.
  • Evaluate and select emerging technologies, vendors, and tools.
  • Optimize BI platform licensing, analytics warehouse queries, and dashboard reporting infrastructure.
  • Develop and maintain the quarterly Data Engineering roadmap, delivering at least 85% of committed quarterly milestones on time and achieving a stakeholder data-product-use satisfaction rating of at least 4 out of 5 semi-annually.
  • Meet the approved hiring plan on schedule and ensure new hires are fully productive within 90 days.
  • Keep annual regretted attrition under 10%, provide each team member with a documented development plan, and achieve at least the company benchmark on team engagement surveys.
  • Pass SOC2 audits with no data-related exceptions and maintain a governance framework covering ownership, lineage, and access controls across production datasets.
  • Manage data platform spending within budget while lowering unit cost per well year over year.

About the Company

Ambyint

AI-driven well optimization software and hardware

Ambyint provides software and hardware that optimize upstream oil and gas wells by integrating with existing SCADA systems and analytics tools to monitor performance and automate decisions. It uses AI and deep data analytics to detect important anomalies and adjust controller settings for lift types such as plunger and rod lift. The company differentiates itself with standardized integration, strong data security, and a user-friendly design focused on lifecycle optimization. Its goal is to raise production rates while lowering operating costs for energy companies.

Company size
51-200
Company stage
Grant
Total funding
$33.1M
Headquarters
Calgary, Canada
Founded
2004

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