Job Title: Sr. Data Engineer
Location: Complete Remote
Type: Fulltime
Experience: 7+ Yrs
Job Description:
Data Engineer – Cloud & Data Platform Engineering Position Summary Highly motivated and technically strong Data Engineer who thrives in fast-paced environments and enjoys solving complex business problems through innovative and scalable data solutions.
The ideal candidate is not just a developer, but an engineer who can quickly learn emerging technologies, adapt to changing business needs, and deliver nimble, reusable, and production-ready solutions. This role requires a strong foundation in modern data engineering, cloud platforms, automation, and software engineering best practices.
Key Responsibilities:
- Serve as the liaison between business stakeholders, support teams, architects, and development teams.
- Lead requirement discussions, clarify business needs, and translate them into technical solutions.
- Manage project deliverables, commitments, timelines, and status reporting.
- Provide regular communication on progress, risks, issues, and mitigation plans.
- Coordinate activities across onshore and offshore teams to ensure successful delivery.
- Handle production incidents, critical issues, and escalations with a sense of urgency and ownership.
- Demonstrate strong decision-making skills in ambiguous and fast-paced environments.
- Drive continuous improvement initiatives and identify opportunities to simplify and optimize existing processes. Strong stakeholder management, communication, ownership mindset, and proven ability to lead delivery in high-demand, fast-paced environments. Data Engineering & Platform Development
Design, develop, and maintain scalable data pipelines using modern cloud technologies. - Build and optimize data ingestion, transformation, and integration frameworks.
- Develop reusable components, utilities, and accelerators that reduce development effort and improve delivery speed.
Implement robust data quality, monitoring, auditability, and operational controls. - Design solutions that support both batch and event-driven processing patterns. Cloud & Modernization Initiatives
Participate in cloud migration and modernization efforts. - Leverage cloud-native services to improve scalability, reliability, security, and cost efficiency.
- Evaluate emerging technologies and recommend innovative solutions aligned with enterprise architecture standards. Engineering Excellence
- Apply software engineering principles to data engineering solutions.
- Drive automation across deployment, testing, monitoring, and operational support processes.
- Continuously improve platform performance, reliability, and maintainability.
- Troubleshoot complex production issues and provide timely resolutions. Collaboration & Leadership
- Partner with business stakeholders, architects, and product teams to translate requirements into technical solutions.
- Mentor junior engineers and promote engineering best practices.
- Contribute to architectural discussions and technology evaluations.
- Foster a culture of continuous learning, innovation, and ownership.
Required Qualifications :
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
- 7+ years of experience in Data Engineering, Software Engineering, or related technical roles.
- Strong experience with SQL, Python and large-scale data processing.
- Hands-on experience with cloud platforms, preferably: o Microsoft Azure
- Experience with modern data platforms such as: o Databricks o Apache Spark
- Strong programming skills in Python.
- Experience designing scalable ETL/ELT pipelines.
- Familiarity with CI/CD [Asset Bundle preferred], DevOps, and Infrastructure- as-Code principles.
- Experience working with relational and distributed data platforms.
Preferred Qualifications:
Experience with:
o Event-driven architectures
o Streaming platforms:
o API integrations
o Data warehousing and lakehouse architectures
Knowledge of:
o Data governance
o Metadata management
o Data quality frameworks
o Observability and monitoring solutions
Experience building reusable frameworks and shared engineering utilities.
Exposure to AI/ML enablement platforms and modern analytics ecosystems.
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