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Data Engineer, Data Migration Project-based engagement | 5 or more years of experience 4 months is the contract duration and based on project progress, extension would be reviewed. Job Overview We are seeking an experienced Data Engineer to take a hands-on role in a business-critical data migration. The business is consolidating a fragmented estate of systems into a single target architecture, and this role sits at the centre of moving live CRM, project and operational data into that target state. This is a migration role rather than a business-as-usual pipeline role. The successful candidate will spend as much time working out what is actually in poorly documented source systems, proving that data has moved across correctly, and standing behind those numbers in front of business and finance stakeholders, as they will building pipelines. The role works alongside a solution architect, an internal DBA team, a data and insights team, and the platform supplier delivering the target system. Delivery is incremental, one business unit at a time, with each unit passing through a gated process: understand, cleanse, map and prepare, test, fix, migrate, validate, and obtain business sign-off before the next unit begins. Key Responsibilities Understanding the source data Profile source systems and establish what is actually held in them: volumes, blanks, duplicates, orphaned records, values that do not conform, and undocumented local practice. Produce a clear data quality baseline per source, with a prioritised clean-up backlog and named owners. Extract data from a mixed estate, including on-premise SQL-backed systems, SaaS platforms via API or export, SharePoint and OneDrive, and spreadsheets at scale. Building the migration Build and maintain extract, staging, transformation and load routines, using Microsoft Fabric components (Workspaces, Lakehouses, SQL Endpoints, Data Pipelines, Notebooks) and Azure Data Factory or Fabric Data Factory. Write and apply clean-up and transformation rules that trace back clearly to an agreed field mapping specification. Design routines to be re-runnable, version controlled and parameterised, so that the next business unit is a configuration change rather than a rebuild. Work alongside the platform supplier and their migration tooling rather than duplicating it. Proving it worked Build and run a reusable set of technical checks ahead of each business sign-off, covering record counts, unique identifiers, links between records, rejected records and finance continuity. Maintain a permanent cross-reference between source and target record identifiers, including which system each record originated in. Reconcile finance data to agreed control totals, including work in progress, unbilled income, debtor balances and ageing, billed-to-date values, income recognised to date, currency and tax treatment, and correct accounting period placement through cutover. Log every rejected record with a reason, drive each one to a decision with a named owner, and evidence re-runs. Produce the evidence pack for business and finance stakeholder sign-off, and explain it clearly to a non-technical audience. Supporting the target design Feed profiling evidence into decisions on the programme data model, data dictionary, common lists, dimensions and master data, so that detailed mapping can proceed. Contribute to entity definitions and shared vocabulary across business units. Making it repeatable Prove the migration runbook through a pilot and capture real elapsed timings, replacing planning estimates with actuals. Support the manual-entry route where records are entered directly by the business, including entry packs, progress tracking, and reconciliation of what has been entered against what should be present. Contribute to the archive and retention register, and to proving that archived data can actually be retrieved before source systems are switched off. Document as the work proceeds, so that the capability outlives the engagement. Requirements 4 or more years as a data engineer, including at least one end-to-end data migration, from discovery through to production cutover and sign-off. Strong SQL skills are mandatory, including profiling, transformation, and reconciliation across large data volumes. Hands-on experience with Azure Data Factory or Fabric Data Factory is mandatory, including building and maintaining ETL/ELT pipelines, orchestration, parameterisation and dynamic content, triggers, monitoring, and error handling. Working knowledge of the Microsoft Fabric ecosystem, including Workspaces, Lakehouses, SQL Endpoints, Data Pipelines, and Notebooks. PySpark, with experience developing scalable transformations. Core data engineering concepts: Lakehouse and Medallion architecture (Bronze/Silver/Gold), Apache Spark architecture, Spark clusters and compute, distributed processing. Data profiling and data .
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.