Senior Data Engineer

Teamified

11d • Remote

About The Position

We are seeking an experienced Senior Data Engineer to design, build, and scale modern cloud-based data platforms that enable trusted analytics, operational reporting, and data-driven decision making across the organization. This role will play a critical part in evolving our data architecture, data engineering practices, and analytics capabilities, with a strong focus on Snowflake as the core enterprise data platform. As a senior member of the data team, you will be responsible for building robust and scalable data pipelines, optimizing data models, improving data quality and governance, and enabling self-service analytics capabilities for business and engineering stakeholders. The ideal candidate combines strong technical depth in modern data engineering practices with a pragmatic mindset, excellent stakeholder engagement skills, and a passion for building reliable and scalable data platforms.

Requirements

• 5+ years of experience in Data Engineering, Analytics Engineering, or related data platform roles.
• Experience designing and supporting multi-region and enterprise-scale data platform architectures.
• Strong experience driving performance optimization and cloud cost efficiency initiatives across large-scale data workloads.
• Strong understanding of platform reliability, operational maturity, resilience, and production support practices.
• Experience implementing advanced governance, security, access control, and data protection models within enterprise data platforms.
• Strong capability in developing architectural standards, engineering documentation, and scalable platform design patterns.
• Strong hands-on expertise with Snowflake in enterprise-scale environments.
• Advanced SQL skills with experience optimizing complex analytical queries and data transformations.
• Strong experience building and maintaining modern ELT/ETL pipelines and orchestration workflows.
• Strong understanding of modern data warehousing concepts, dimensional modelling, and scalable data architecture.
• Experience working with cloud platforms such as AWS, Azure, or GCP.
• Experience with data transformation and orchestration tools such as dbt, Airflow, Fivetran, Matillion, or equivalent platforms.
• Experience integrating structured and semi-structured data sources.
• Strong understanding of data governance, security, and access management principles.
• Proven ability to manage large and complex datasets in production environments.
• Strong analytical, troubleshooting, and problem-solving capabilities.
• Excellent communication and stakeholder engagement skills.
• Ability to work effectively in fast-paced, agile, and collaborative environments.

Nice To Haves

• Experience within fintech, payments, SaaS, or highly regulated industries.
• Experience with real-time data streaming technologies such as Kafka or Kinesis.
• Exposure to machine learning data pipelines and advanced analytics workloads.
• Experience implementing CI/CD practices for data engineering workflows.
• Familiarity with Infrastructure-as-Code tools such as Terraform.
• Experience with data observability and quality tooling.
• Exposure to compliance frameworks such as PCI-DSS, ISO27001, or SOC 2.
• Experience mentoring engineers or leading technical initiatives.

Responsibilities

• Design, build, and maintain scalable and secure cloud-native data platforms and data pipelines.
• Lead the architecture, optimization, and operational management of the Snowflake data warehouse platform.
• Develop robust ELT/ETL pipelines to ingest, transform, and deliver high-quality data from multiple internal and external sources.
• Build reusable and maintainable data frameworks, transformation models, and orchestration workflows.
• Develop and maintain infrastructure-as-code and automation for data platform provisioning and management where appropriate.
• Optimize performance, scalability, and cost efficiency across data storage, transformation, and query workloads.
• Support near real-time and batch-based data processing requirements.
• Own and continuously improve Snowflake architecture, performance tuning, security, governance, and operational best practices.
• Design and optimize Snowflake schemas, warehouses, clustering strategies, and data sharing capabilities.
• Implement scalable data modelling approaches including dimensional modelling and data vault methodologies where appropriate.
• Manage Snowflake access controls, roles, permissions, and secure data sharing practices.
• Monitor Snowflake usage, query performance, and cost consumption to drive optimization initiatives.
• Support data lifecycle management, retention, and governance policies within Snowflake.
• Design and maintain curated, trusted, and scalable data models to support analytics, reporting, and operational use cases.
• Partner with analysts, business stakeholders, and engineering teams to translate business requirements into scalable data solutions.
• Enable self-service analytics capabilities through well-structured semantic layers and governed datasets.
• Support and optimize BI and reporting platforms such as Looker, Power BI, or equivalent tools.
• Ensure data structures and models support both operational reporting and strategic analytics requirements.
• Implement data quality controls, validation frameworks, reconciliation processes, and monitoring capabilities.
• Proactively identify and resolve data integrity, consistency, and performance issues.
• Establish observability and operational monitoring for data pipelines and platform reliability.
• Contribute to data governance, lineage, cataloguing, and metadata management practices.
• Ensure data platforms and engineering processes comply with security, privacy, and regulatory requirements.
• Collaborate closely with Product, Engineering, Operations, Finance, and Business stakeholders to deliver impactful data solutions.
• Mentor and support junior engineers and analysts within the broader data function.
• Contribute to data engineering standards, best practices, and platform strategy.
• Drive continuous improvement initiatives across data architecture, tooling, and delivery practices.
• Work cross-functionally to improve organizational data literacy and data maturity.

Benefits

• Flexibility in work hours and location, with a focus on managing energy rather than time.
• Access to online learning platforms and a budget for professional development
• A collaborative, no-silos environment, encouraging learning and growth across teams
• A dynamic social culture with team lunches, social events, and opportunities for creative input
• Health insurance
• Leave Benefits
• 13th Month Salary

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed