Job Summary: We are looking for a skilled Data Engineer with strong hands-on experience in AWS to design, build, and maintain scalable data pipelines and cloud-based data platforms. The ideal candidate will have expertise in modern data warehousing, ETL/ELT development, and distributed data processing while ensuring data quality, performance, and security. Key Responsibilities: ·      Design, develop, and maintain scalable data pipelines using AWS services. ·      Build and optimize ETL/ELT workflows for structured and unstructured data. ·      Implement data lakes and data warehouses on AWS. ·      Work with large datasets to ensure high performance, reliability, and data integrity. ·      Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements. ·      Perform data modeling for analytical and operational use cases. ·      Ensure data quality, governance, and security best practices. ·      Monitor and troubleshoot data workflows and production issues. ·      Support CI/CD and automation for data platform deployments. Required Skills & Experience: ·      Strong hands-on experience with AWS services such as S3, Glue, Redshift, Lambda, EMR, Athena, and RDS. ·      Proficiency in Python and SQL for data processing and analysis. ·      Experience in building and optimizing ETL/ELT pipelines. ·      Solid understanding of data warehousing and data lake architecture. ·      Experience with Apache Spark / PySpark. ·      Knowledge of workflow orchestration tools (Airflow or similar). ·      Familiarity with streaming frameworks (Kinesis/Kafka) is a plus. ·      Experience with data modeling and performance tuning. ·      Understanding of DevOps, CI/CD, and infrastructure as code (Terraform/CloudFormation). ·      Experience working in Agile/Scrum environments. Good to Have: ·      Experience with Snowflake on AWS. ·      Exposure to real-time data processing. ·      AWS certification (e.g., AWS Certified Data Analytics / Solutions Architect). Soft Skills: ·      Strong problem-solving and analytical skills. ·      Excellent communication and stakeholder management. ·      Ability to work in a fast-paced, collaborative environment.

• Design and build robust data pipelines and feature stores that powers our AI products. • Analyse data requirements, understand complex data sources, and partner with architects to determine the best methods to extract, transform, and load data into data platform. • Lead conversations with business or functional teams to translate user requirements into reusable and cost-efficient data acquisition methodologies for AI products. • Be the key anchor for data extraction, preparation and hosting processes representing the AI team. Data expert for engineering datasets used by data scientists working on various use cases. • Perform functional and stress testing on data models and extraction jobs to ensure that design is optimized for performance, scale, and reusability. • Manage foundational data administration tasks such as scheduling jobs, troubleshooting job errors, identifying issues with job windows, assisting with database backups and performance tuning. • Ensure data quality throughout entire development process, including audits and feedback loops to sources of truth. • Create or update technical documentation for transition to support teams. • Mentor junior data engineers

• Architect and implement the full data foundation powering an internal LLM-driven analytics platform integrated directly with Amazon Seller Central. • Own ingestion, normalization, warehousing, semantic modeling, and query-ready access across commerce, advertising, operational, and financial datasets spanning multiple client brands. • Establish and manage authenticated SP-API connections across client brands. • Ingest data from Orders, Reports, Advertising, Inventory, and Financial endpoints. • Handle throttling, pagination, retries, and rate-limit constraints reliably. • Implement incremental loads, historical backfills, and failure recovery. • Continuously expand usable Amazon dataset coverage. • Design and implement scalable ELT/ETL pipelines from raw ingestion to analytics-ready schemas. • Stand up and operate a centralized warehouse (e.g., Snowflake, BigQuery, Redshift, Postgres, or equivalent). • Normalize multi-client, multi-brand datasets with historical retention and versioning. • Model core domains including sales, advertising performance, inventory/FBA, and profitability. • Ensure long-term maintainability, observability, and scale readiness. • Define standardized ecommerce metrics (sales, net revenue, ad spend, TACoS, and related drivers). • Create curated semantic or mart layers suitable for deterministic LLM querying. • Implement guardrails for filters, date logic, and metric definitions. • Ensure query outputs are reproducible, traceable, and grounded in source-of-truth tables. • Partner with founders on LLM model and architecture decisions. • Implement validation, anomaly detection, and freshness SLAs. • Maintain logging, alerting, and incident-response readiness across pipelines. • Ensure auditability and accuracy of analytics outputs. • Proactively resolve reliability risks and operational gaps. • Document API integrations, schemas, refresh cadences, and assumptions. • Produce clear handoff materials enabling internal continuity. • Prepare architecture for secure multi-tenant, external-facing product evolution. • Maintain a documentation-first, long-term ownership mindset. • Work directly with the client’s founders as the primary technical owner of the data platform, collaborating on architecture, LLM strategy, and product readiness. • Receive guidance from the Operations Manager to support delivery excellence, professional growth, and long-term success in the role.

- Own CRM data hygiene and maintain high-quality contact and account data. - Audit and optimise CRM properties, lifecycle stages, and deduplication processes. - Build and maintain scalable enrichment workflows in Clay. - Run enrichment waterfalls (email, phone, LinkedIn, firmographics). - Manage scraping workflows using Bright Data and Apollo exports. - Prepare outbound lists and ensure they are campaign-ready with low bounce rates. - Reduce duplicate and stale records across CRM and data sources. - Implement and manage email validation workflows. - Maintain reporting on list health, data completeness, and coverage metrics. - Document workflows and create repeatable, scalable processes. - Run light scripts or technical executions where needed.