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Senior Engineering Manager, Data Analytics – Data & AI

Engineering ManagerEngineering ManagerFull TimeRemoteTeam 1,001-5,000Since 2011H1B No SponsorCompany SiteLinkedIn

Location

Wyoming

Posted

52 days ago

Salary

$146K - $277K / year

Postgraduate Degree8 yrs expEnglishAmazon RedshiftBig QueryETLJavaPythonScalaSQLTableau

Job Description

• Drive decision making in building cutting-edge products. • Transform large datasets from complex systems, generate actionable insights, and share the results with stakeholders at all levels of the company. • Partner and lead projects with product management, engineering, client engagement, finance, and other enterprise level teams. • Build out analytics and management report dashboards by collaborating closely with directors and cross functional teams to infuse analytics insights into major business priorities and enable data driven decisions. • Partner closely with data engineering and enterprise systems team to define, plan, implement, and support the data infrastructure. • Lead the definition of data requirements and controls to enable the integrity of the company’s analytics and management reporting. • Drive projects and initiatives for assessment of current data and process infrastructure to support team objects. • Support data analysis and reporting requirements, building and maintaining knowledge repository and technical documentation libraries for data lake and reporting. • Run standup, workgroup meetings, identifying action items and addressing blockers to keep projects on track.

Job Requirements

  • Master’s degree or the equivalent in Computer Science, Management Information Systems, or a related field plus 8 years of experience in data analytics or related experience.
  • Experience with Data pipelines, ETL/ELT, data warehousing, and orchestration tools; BI tools (Tableau, Looker or similar);
  • Data platforms such as Snowflake, Big Query, Redshift; and observability and experiment platforms;
  • Statistics experimentation (A/B testing), modeling techniques and casual inference;
  • Languages such as Python, SQL, Scala, or Java for data systems;
  • Hiring a technical team, including providing feedback and mentoring for career growth;
  • Translating business problems into technical plans;
  • Driving strategy for data products and platforms, such as self-serve analytics;
  • Balancing long-term tech investments with short-term business needs;
  • Agile or hybrid methods tailored to analytics projects;
  • Working with product and business leaders to define KPIs, dashboards, and success metrics for product performance;
  • Aligning data strategy with company and product goals;
  • Driving data quality, engineering best practices, and scalable systems.

Benefits

  • Offers Equity
  • Offers Bonus

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