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Key Responsibilities and Required Skills for Open Application

💰 $ - $

🎯 Role Definition

We are building a talent pool for an Open Application opportunity that supports product development, data engineering, operations, and project delivery across fast-paced, cross-functional teams. This role is ideal for candidates who can own end-to-end initiatives, write maintainable code or processes, translate business requirements into technical solutions, and communicate clearly to stakeholders. Responsibilities will be tailored to your strengths — common assignments include software development, data pipeline design, analytics, product support, and process optimization. The position demands strong execution, continual learning, and a bias for measurable outcomes.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Software Engineer / Graduate Software Developer
  • Data Analyst or Junior Data Engineer
  • Operations Coordinator or Project Coordinator

Advancement To:

  • Senior Engineer / Senior Data Engineer
  • Product Manager / Technical Product Owner
  • Technical Lead / Engineering Manager
  • Data Architect / Analytics Lead

Lateral Moves:

  • DevOps / Platform Engineer
  • Customer Success or Solutions Engineering
  • Business Analyst / Growth Analyst

Core Responsibilities

Primary Functions

  • Lead the end-to-end delivery of technical features or operational initiatives by interpreting business requirements, designing a pragmatic solution, estimating effort, and coordinating implementation across engineering, product, QA, and operations teams.
  • Design, build, test, and maintain reliable, scalable code and services using modern languages and frameworks; follow best practices for code quality, automated testing, and continuous integration/continuous deployment.
  • Architect and implement robust data pipelines and ETL processes to ingest, cleanse, transform, and validate data from multiple sources to enable analytics, reporting, and machine learning workloads.
  • Use SQL and programmatic data processing (e.g., Python, Spark) to analyze large datasets, generate insights, and produce reproducible analytics deliverables that inform business decisions and product roadmaps.
  • Own feature rollouts, release planning, and coordination with cross-functional stakeholders to ensure timely launches with observability, monitoring, and rollback strategies in place.
  • Define and track success metrics (KPIs, user engagement, performance, error rates) and produce recurring dashboards and executive-ready summaries to measure impact and inform iterative improvements.
  • Investigate and resolve production incidents, perform root cause analyses, implement remediation plans, and document postmortems to prevent recurrence and improve system reliability.
  • Translate ambiguous business goals into clear technical requirements and acceptance criteria; prepare user stories, wireframes, and detailed technical specs where appropriate.
  • Perform code and design reviews, mentor junior engineers or analysts, and establish team standards for maintainability, readability, and performance.
  • Integrate third-party APIs, SDKs, and vendor systems securely and reliably, managing authentication, rate limits, and error handling while ensuring compliance with contractual requirements.
  • Implement and enforce data governance, security controls, and privacy best practices, including role-based access, encryption, and audit logging consistent with compliance needs.
  • Drive automation across repetitive operational tasks, reducing manual effort by introducing scripts, workflows, and platform tools to improve velocity and reduce error rates.
  • Collaborate with product managers and UX designers to prototype and validate user-facing features, perform A/B tests, and iterate based on quantitative and qualitative feedback.
  • Optimize costs and performance of cloud infrastructure by right-sizing resources, leveraging autoscaling, caching, and database optimizations, and proposing cost-saving architecture changes.
  • Create and maintain comprehensive technical documentation, runbooks, API docs, and onboarding materials to accelerate team productivity and reduce knowledge silos.
  • Partner with data science and analytics teams to productionize models, implement feature stores, and establish monitoring for model drift and inference performance.
  • Lead cross-functional workshops and stakeholder alignment sessions to prioritize backlogs, define minimum viable products (MVPs), and align roadmap expectations with business outcomes.
  • Manage vendor relationships and contracts for platform services, monitoring SLAs and coordinating integrations or escalations when service issues arise.
  • Ensure accessibility, localization, and internationalization considerations are addressed for user-facing features, and collaborate with legal/compliance for regulatory requirements.
  • Conduct performance tuning for databases, search systems, and high-throughput services; profile and remediate hotspots in code and infrastructure.
  • Actively participate in sprint planning, retrospective ceremonies, and continuous improvement initiatives to refine team processes, reduce cycle time, and increase predictability.
  • Recruit, interview, and help onboard new team members; sponsor learning paths and career growth initiatives to retain top talent.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Prepare quarterly reports and business updates for stakeholders including leadership and external partners.
  • Provide technical support to customer success and sales teams for proof-of-concepts and technical due diligence.
  • Maintain compliance-ready documentation for audits and security assessments.
  • Train non-technical stakeholders on dashboards, data interpretation, and self-service analytics tools.
  • Identify process gaps and propose operational playbooks to reduce escalation volume and improve time-to-resolution.
  • Support migration projects (e.g., cloud migration, database upgrades) by planning cutover windows, running validation checks, and coordinating rollback plans.
  • Conduct vendor evaluations, pilot new tools, and recommend adoption plans based on cost-benefit and security posture.
  • Champion knowledge sharing sessions, brown-bags, and internal tech talks to elevate team capabilities.

Required Skills & Competencies

Hard Skills (Technical)

  • Strong programming proficiency in at least one modern language (Python, Java, JavaScript/TypeScript, or Go) with experience writing production-grade code.
  • Advanced SQL skills: complex joins, window functions, query optimization, and experience with OLAP/OLTP systems.
  • Cloud platform experience (AWS, GCP, or Azure) including compute, storage, networking, managed databases, and IAM.
  • Experience designing and maintaining data pipelines and ETL tooling (Airflow, dbt, Spark, Kafka, or similar).
  • Familiarity with containerization and orchestration (Docker, Kubernetes) and CI/CD tools (Jenkins, GitHub Actions, GitLab CI).
  • Knowledge of observability and monitoring tools (Prometheus, Grafana, Datadog, New Relic) and logging (ELK/EFK).
  • Experience with data warehouses and analytics platforms (Snowflake, BigQuery, Redshift) and BI tools (Looker, Tableau, Power BI).
  • API design and integration experience (RESTful APIs, GraphQL, webhook architectures) and authentication patterns (OAuth2, JWT).
  • Version control best practices with Git and experience with branching strategies and pull request workflows.
  • Understanding of software testing strategies: unit tests, integration tests, end-to-end tests, and test automation frameworks.
  • Security and compliance knowledge: encryption, secure coding practices, data privacy regulations (GDPR, CCPA), and IAM controls.
  • Performance tuning expertise for databases, caches (Redis/Memcached), and backend services.
  • Familiarity with machine learning lifecycle basics and ML ops tools (model deployment, monitoring, feature stores) is a plus.
  • Experience with project tracking and collaboration tools (Jira, Confluence, Trello) and agile methodologies.

Soft Skills

  • Excellent stakeholder management: synthesize requirements from product, business, and customer stakeholders and set realistic expectations.
  • Clear written and verbal communication skills for technical and non-technical audiences; able to prepare executive summaries and technical runbooks.
  • Strong problem-solving and debugging skills with an analytical mindset and attention to detail.
  • Collaboration and teamwork: proven ability to work effectively in cross-functional, remote, or distributed teams.
  • Ownership mentality and bias for action — takes accountability for outcomes and drives work to completion.
  • Adaptability and continuous learning — comfortable working in ambiguous environments and quickly adopting new tools and practices.
  • Prioritization and time management — balances competing requests, outages, and feature work while maintaining quality.
  • Mentorship and coaching — ability to grow teammates through constructive feedback and knowledge sharing.
  • Customer-centric thinking — designs solutions with end-user experience, performance, and reliability in mind.
  • Strategic thinking — aligns technical decisions with business goals and contributes to roadmap prioritization.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, Business Analytics, or related field; equivalent practical experience will be considered.

Preferred Education:

  • Master’s degree in a technical or quantitative discipline (Computer Science, Data Science, Business Analytics) or an MBA for product/strategy-focused roles.

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Analytics / Statistics
  • Information Systems / Information Technology
  • Business / Operations / Industrial Engineering
  • Applied Mathematics / Physics / Economics

Experience Requirements

Typical Experience Range:

  • 2–8+ years depending on the level applied for (entry-level to senior roles). We accept candidates with varied backgrounds who demonstrate relevant hands-on experience.

Preferred:

  • 3–5+ years for mid-level contributors; 5–10+ years for senior or lead roles with demonstrated ownership of large-scale systems, cross-functional project leadership, and measurable impact on product or operational KPIs.

If you are submitting an Open Application, please include a tailored résumé, a brief summary of the types of roles you’re open to (engineering, data, product, operations), two examples of projects or deliverables you led, and links to any relevant work samples (GitHub, portfolio, dashboards, whitepapers). We value growth mindset, demonstrable impact, and strong collaboration skills.

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