Data Engineer

Job Description - Data Engineer You should apply if you have: 3 years of experience as a Data Engineer, Analytics Engineer, or in a similar data-platform role. Strong SQL and Python, with the ability to build, debug, and optimize production data pipelines. Hands-on experience with a cloud data stack — AWS preferred (S3, Glue, Redshift) — and modern ELT tooling. Experience ingesting data from diverse sources (transactional databases, SaaS APIs, file dumps) into a warehouse or lakehouse. Exposure to transformation frameworks like dbt and orchestration tools (Airflow, Glue, or similar). A reliability mindset — you care about data freshness, quality, monitoring, and infrastructure cost. The ability to work independently and own systems end-to-end in a build-from-scratch environment. An AI-first mindset and comfort using AI tools (Copilot, Claude, etc.) to accelerate engineering work. You should not apply if you: Prefer working only on well-defined tickets over designing systems from scratch. Are uncomfortable owning production pipelines, reliability, or debugging messy real-world data. Need constant direction and struggle with ambiguity or greenfield problems. See data engineering as just "moving data" rather than enabling business decisions. Are resistant to feedback, learning new tools, or leveraging AI to improve your productivity. Prefer babysitting legacy jobs over building scalable, automated, version-controlled pipelines. Are uncomfortable with cost and ownership accountability in a fast-paced, high-growth environment. Don't enjoy collaborating with analysts and stakeholders to understand what the data is for. Are looking for a narrow role rather than one that demands ownership, curiosity, and continuous improvement. Skills Required: Technical Skills Advanced SQL (joins, CTEs, window functions, query optimization, aggregations) Python for data pipelines and automation Cloud data platforms — AWS (S3, Glue, Redshift) preferred ELT/ETL design and orchestration (Airflow, Glue, or similar) Data lakehouse concepts — S3 / Apache Iceberg (good to have) dbt and data modeling (preferred) Data ingestion from RDBMS, APIs, and file sources; managed connectors like Fivetran (good to have) Data quality, testing, and freshness/uptime monitoring Version control and CI/CD for data workflows (Git) Understanding of data warehousing and dimensional modeling B usiness & Analytical Skills Strong problem-solving and systems thinking Ability to translate analytics and business needs into reliable data models and pipelines Cost-awareness — right-sizing compute and storage Comfort collaborating across analysts, engineers, and business stakeholders Experience working with D2C, E-commerce, Retail, or Consumer business data (preferred) What will you do? Build and own the ingestion layer — land core sources (RDS, Vinculum, Tally, marketplaces) reliably into our S3-Iceberg Bronze lakehouse.…