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Key Responsibilities Own and operate the complete AWS data hub: Bronze (S3, raw partitioned data), Silver (Apache Iceberg on S3, ACID transactions, domain tables), and Gold (Amazon Athena views with business rules applied – Sales Performance, Loyalty Health, Campaign ROI, Staff Performance, Financial View, Unified Customer). Design, build, and own the Customer Data Platform (CDP): architect the unified customer profile store on AWS, implement identity resolution logic (matching customers across clients’s main chain, Beauty, Fashion, and Sleep concepts by email, phone, loyalty member ID, and device identifiers), and maintain a golden customer record per individual across all touchpoints. Manage CDP data ingestion: ingest customer event data from all source systems – POS transactions, e-commerce orders, loyalty events from Eagle Eye, CRM interactions, and web/app behavioural data – into the CDP in near-real-time or batch as appropriate per source. Manage CDP outbound syncs: push enriched customer profiles, segment memberships, and loyalty tier data from the CDP to Eagle Eye (loyalty execution), the marketing platform (campaign targeting), and the AI/Agent Engineer's Gold layer views (personalisation and agent use cases). Manage all data ingestion pipelines: AWS Glue Python ETL jobs, AWS DMS replication, Lambda event-driven ingestion, and scheduled batch pulls, covering all client’s source systems. Govern Lake Formation security: row-level and column-level access controls per consumer role; monthly access reviews; PDPA and UU PDP compliance for all customer PII fields. Maintain the AWS Glue Data Catalog: schema registration, schema evolution management, partition projections, and table properties. Orchestrate pipelines via AWS Step Functions: scheduling, retry logic, failure alerting, and inter-job dependencies. Maintain and extend business intelligence dashboards and reports connected directly to the Gold Athena layer. Collaborate with the AI/Agent Engineer: ensure Gold layer views and CDP customer profiles are structured for effective MCP natural language query generation and personalisation agent use cases. Manage the Azure-to-AWS data bridge for client’s hybrid cloud architecture: design and operate pipelines that replicate Microsoft system data (D365 F&SCM transactions, Dataverse/Power Pages records, SharePoint document metadata) from Azure into the AWS Bronze layer. Govern cross-cloud data consistency: ensure that key business entities remain consistent across AWS and Azure; define and monitor reconciliation checks that flag divergence between the two platforms. Shadow CloudMile during ECI build phase; produce client’s internal architecture runbooks before handover. Required Qualifications & Experience Minimum 5 years of data engineering experience; at least 3 years on AWS data platforms in production, not training environments. Demonstrable production experience with AWS Glue, Amazon S3, Amazon Athena, and AWS Lake Formation. Hands-on experience with Apache Iceberg or Apache Hudi on S3: ACID transactions, schema evolution, time travel. Experience designing or building a Customer Data Platform or unified customer profile store: identity resolution, profile merging, and customer event ingestion from multiple source systems. Advanced SQL: complex Athena queries, window functions, CTEs, partition optimisation. English proficiency at B2 minimum (IELTS 6.0); mandatory assessment at interview. Strong independence: able to diagnose pipeline failures, identify data quality risks proactively, and own the platform without a team around them. Technical Proficiency AWS: S3, AWS Glue (Python ETL, Data Catalog, Data Quality), AWS DMS, AWS Step Functions, Amazon Athena, AWS Lake Formation, Amazon CloudWatch, CloudTrail, AWS Lambda, Amazon DynamoDB. CDP architecture: unified customer profile design, identity resolution logic, customer event schema design, real-time and batch ingestion patterns, and outbound sync to loyalty and marketing systems. Apache Iceberg: snapshot management, schema evolution, compaction, time travel. Python (intermediate–advanced): ETL scripting, schema standardisation, deduplication, identity resolution. Azure Data Factory (ADF): sufficient working knowledge to design and operate data pipelines bridging Microsoft Azure sources into AWS S3 (leeway given for learning if experience is rare). BI reporting tools: experience with at least one cloud-connected BI platform (e.g., Amazon QuickSight, Power BI, or equivalent). Git / Azure DevOps.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.