As a Senior Data Engineer, you will play a crucial role in building and maintaining the foundation of our data ecosystem. You’ll work alongside data engineers, analysts, and product teams to create robust, scalable, and high-performance data pipelines and models. Your work will directly impact how we deliver insights, power product features, and enable data-driven decision-making across the company.

This role is perfect for someone who combines deep technical skills with a proactive mindset and thrives on solving complex data challenges in a collaborative environment.

Challenges You’ll Meet:

• Pipeline Development and Optimization: Build and maintain reliable, scalable ETL/ELT pipelines using modern tools and best practices, ensuring efficient data flow for analytics and insights.

• Data Modeling and Transformation: Design and implement effective data models that support business needs, enabling high-quality reporting and downstream analytics.

• Collaboration Across Teams: Work closely with data analysts, product managers, and other engineers to understand data requirements and deliver solutions that meet the needs of the business.

• Ensuring Data Quality: Develop and apply data quality checks, validation frameworks, and monitoring to ensure the consistency, accuracy, and reliability of data.

• Performance and Efficiency: Identify and address performance issues in pipelines, queries, and data storage. Suggest and implement optimizations that enhance speed and reliability.

• Security and Compliance: Follow data security best practices and ensure pipelines are built to meet data privacy and compliance standards.

• Innovation and Continuous Improvement: Test new tools and approaches by building Proof of Concepts (PoCs) and conducting performance benchmarks to find the best solutions.

• Automation and CI/CD Practices: Contribute to the development of robust CI/CD pipelines (GitLab CI or similar) for data workflows, supporting automated testing and deployment.

You Should Have:

• 4+ years of experience in data engineering or backend development, with a strong focus on building production-grade data pipelines.

• Solid experience working with AWS services (Redshift, Spectrum, S3, RDS, Glue, Lambda, Kinesis, SQS).

• Proficient in Python and SQL for data transformation and automation.

• Experience with dbt for data modeling and transformation.

• Good understanding of streaming architectures and micro-batching for real-time data needs.

• Experience with CI/CD pipelines for data workflows (preferably GitLab CI).

• Familiarity with event schema validation tools/ solutions (Snowplow, Schema Registry).

• Excellent communication and collaboration skills. Strong problem-solving skills—able to dig into data issues, propose solutions, and deliver clean, reliable outcomes.

• A growth mindset—enthusiastic about learning new tools, sharing knowledge, and improving team practices.

Tech Stack You’ll Work With:

• Cloud: AWS (Redshift, Spectrum, S3, RDS, Lambda, Kinesis, SQS, Glue, MWAA)

• Languages: Python, SQL

• Orchestration: Airflow (MWAA)

• Modeling: dbt

• CI/CD: GitLab CI (including GitLab administration)

• Monitoring: Datadog, Grafana, Graylog

• Event validation process: Iglu schema registry

• APIs & Integrations: REST, OAuth, webhook ingestion

• Infra-as-code (optional): Terraform

Bonus Points / Nice to Have:

• Experience with additional AWS services: EMR, EKS, Athena, EC2.

• Hands-on knowledge of alternative data warehouses like Snowflake or others.

• Experience with PySpark for big data processing.

• Familiarity with event data collection tools (Snowplow, Rudderstack, etc.).

Interest in or exposure to customer data platforms (CDPs) and real-time data workflows.