Data Engineer

• Data modeling & transformation
• Build and maintain DBT models in Snowflake — design layered architecture (staging, intermediate, marts), write data quality tests (Elementary)
• Write clean, well-structured Python and SQL; participate in code reviews and help define engineering standards for the data team
• Design and maintain data pipelines using Airflow (DAGs in Python)
• Manage data integrations via Airbyte — starting with marketing sources (GA4, Facebook Ads, TikTok Ads) and expanding to other streams over time
• Use AWS S3 as the primary storage layer — manage buckets, partitioning, and access control
• Own CI/CD pipelines for data workflows — automated testing, deployment, and monitoring
• Monitor data quality and pipeline reliability; proactively identify and fix issues before they reach downstream consumers
• Work closely with analysts to understand their data needs and translate them into well-modeled datasets
• Bridge the gap between raw data and analyst-ready tables — create models clearly so analysts can work independently

• Designing and developing scalable and efficient data pipelines, ETL processes, and data integration solutions.
• Ensuring data quality and reliability by implementing data validation, cleansing, and quality monitoring processes.
• Optimising database performance by tuning queries, implementing indexing strategies, and analyzing performance metrics.
• Collaborating with cross-functional teams to gather requirements, understand data needs, and develop solutions.
• Staying up-to-date with emerging technologies and industry trends in data engineering.
• Establishing and enforcing best practices and standards for data engineering.

Senior Data Engineer

• Lead the design and evolution of our core data architecture across transactional, analytical, and long-term storage systems.
• Own and improve PostgreSQL data design, schema structure, indexing strategy, query performance, relationships, constraints, and data integrity.
• Design, review, and maintain ERDs and data models that clearly represent business logic, system behavior, and domain relationships.
• Work closely with backend engineers to ensure application data models are scalable, consistent, and suitable for both operational and analytical use cases.
• Build and improve data pipelines from production systems into analytics databases and reporting layers.
• Design reliable approaches for data ingestion, replication, transformation, validation, rollups, aggregations, and historical storage.
• Help define the right architecture for hot, warm, and cold data storage.
• Support the design of analytics-friendly datasets for reporting, dashboards, product analytics, finance, compliance, and operational monitoring.
• Help evolve our analytics database strategy, including how we use analytical stores, materialised views, denormalised tables, and aggregation layers.
• Establish data modeling best practices across the engineering team.
• Identify and fix data quality issues, including inconsistent schemas, duplicated logic, missing relationships, poor naming, and unreliable reporting sources.
• Collaborate with backend, product, DevOps, and leadership teams to turn business requirements into clean, scalable data structures.
• Define best practices for migrations, data retention, archival, backfills, and safe deletion of production data.
• Mentor engineers on database design, ERDs, data modeling, and data pipeline thinking.

Data Platform Lead

• Build scalable and reliable data pipelines that provide accurate data feeds from internal and external systems.
• Govern scalable & performant cloud-deployed production relational and non-relational databases.
• Collaborate on architecture definitions, always thinking of solutions that are scalable and secure.
• Drive data systems to be as near real-time as possible.
• Design, document, automate and execute test plans to ensure the quality of the datasets.
• Participate in the process of generating and analyzing features.

• Collaborate with blockchain engineers to index blockchain data and create robust data pipelines
• Efficiently parse and index a variety of data formats, ensuring high performance and low latency
• Document technical processes and solutions to ensure knowledge sharing across the team
• Work with the Solana Improvement Document process to make sure updates are smooth and don’t see regressions during testing or in live environments
• Help gather and formulate scores for informative metrics like decentralization scores, transaction inclusion performance, and economics

• Own the infrastructure that turns raw events from millions of mobile users into trustworthy data the business runs on.
• Design and scale the pipelines, warehouses, and tooling.
• Work side-by-side with analytics, product, and growth teams.
• Design, build, and maintain scalable data architecture.
• Own ingestion, transformation, storage, and modelling.
• Write clean, efficient Python to power reliable pipelines.
• Lead our dbt project on BigQuery.
• Partner with analytics to ship Metabase dashboards.
• Set the bar for what 'trustworthy data' means here.

• Architect high-performance spatial data pipelines
• Lead a technical team
• Handle massive datasets
• Optimize geometry storage
• Build scalable backend logic

Lead Geospatial Data Engineer

• Deliver end-to-end HR data migration activities for the SuccessFactors implementation.
• Design, develop, and execute data extraction, transformation, and loading (ETL) processes.
• Support payroll parallel testing by preparing and loading necessary data sets.
• Manage and execute delta loads to ensure data synchronisation and accuracy.
• Perform data cleansing, validation, and reconciliation activities.
• Collaborate with functional consultants and business users to understand data requirements and resolve discrepancies.
• Document data migration processes, mappings, and validation rules.

Data Migration Consultant

• Design, build, and maintain data pipelines (Airflow, Airbyte, DBT, Snowflake)
• Implement Medallion architecture (bronze / silver / gold layers)
• Develop reliable ETL/ELT processes and integrate data from multiple sources (SQL/noSQL/APIs)
• Improve data modeling practices and overall data quality
• Optimize performance and troubleshoot issues
• Collaborate closely with analysts and business teams to turn data into insights

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Data Engineer

Senior Data Engineer

Data Platform Lead

Data Engineer

Senior Data Engineer

Data Engineer

Lead Geospatial Data Engineer

Data Migration Consultant

Senior Data Engineer