You might be our missing piece if you have:

• 5+ years of experience in data engineering, database development, and cloud-based data solutions (especially on AWS)

• Strong proficiency in SQL (T-SQL, PL/SQL) and experience with database technologies (e.g., Oracle, SQL Server, Snowflake/Redshift)

• Hands-on experience with ETL/ELT tools and frameworks, including modern cloud integration services (e.g., AWS Glue, Apache Airflow or Azure Data Factory)

• Experience with Snowplow or similar event data tracking pipelines, including their implementation, maintenance, and optimization for behavioral data collection and analytics

• Experience with data modeling, data integration, and data warehousing concepts

• Strong programming skills in Python (e.g., Pandas, automation scripting) for ETL pipeline development

• Knowledge of data governance and data quality frameworks, as well as security best practices for data (e.g., GDPR)

• Experience working in Agile development environments, with collaborative tools and iterative processes

We would be thrilled if you have:

• Strong understanding of data lake architectures and advanced cloud-based data solutions (with depth in AWS data services)

• Familiarity with high-performance query optimization techniques for large-scale datasets (e.g., query tuning, indexing strategies)

• Exposure to data governance and metadata management systems (e.g., data catalog, lineage tools)

• Experience with real-time streaming data frameworks and messaging systems (e.g., Apache Kafka, Amazon Kinesis) for event processing and ingestion

• Knowledge of big data processing frameworks (e.g., Apache Spark or Flink) and experience with CI/CD pipelines or Infrastructure-as-Code for data engineering projects

We will be working together on:

• Designing, developing, and maintaining data pipelines – including both batch ETL processes and real-time streaming solutions – to support our product teams. This includes implementing and managing Snowplow-based event tracking pipelines to collect, validate, and process user behavioral data in real time for analytics and product insights

• Collaborating with cross-functional teams (product managers, analysts, data scientists, etc.) to understand data needs and deliver insightful, scalable data solutions

• Replicating and generalizing successful data pipeline patterns to accelerate new pipeline development and ensure consistency and reliability across projects

• Developing reusable data processing utilities and tooling (leveraging common data-centric libraries and frameworks in Python) to streamline ETL/ELT workflows

• Optimizing database performance and ensuring high reliability of our data stores by performing query optimization, indexing, and tuning of SQL queries

• Monitoring and enhancing Snowplow pipeline performance and data quality – troubleshooting pipeline issues, optimizing event collection, and implementing improvements to maximize uptime and data accuracy

• Supporting data governance and quality initiatives to maintain data integrity, privacy compliance, and consistency across all data pipelines

• Providing technical guidance on data integration, transformation, and analytics best practices to team members and stakeholders

• Building reports and dashboards (in collaboration with BI/Analytics teams) to empower product and business teams with actionable insights from collected data

• Acting as a data solutions expert for the organization – advising and assisting product teams in selecting the right data architectures, tools, and approaches (e.g., choosing the appropriate data storage, streaming service, or analytics tool for a given need)

• Translating business and user requirements into technical specifications by working closely with product managers and engineers to ensure data solutions meet real-world needs