Data Driven Engineer Job at Demant (Smørum)

Data-Driven Adaptation Engineer

Location

India , Gurgaon

Salary:

Not provided

Randstad

Expiration Date

July 23, 2026

Requirements

Degree in Computer Science, Electrical Engineering, Mathematics, Data Science, Physics, or related field
Solid foundation in statistics, data analysis, and analytical problem-solving
Hands-on experience with Python (pandas, numpy, matplotlib, Jupyter)
Experience working with Databricks, PySpark, or similar data platforms
In-depth understanding of state-of-the-art software development practices (linting, code style, version control systems)
Ability to understand and work with algorithms and signal/data processing workflows
Very good English skills
German is a plus

Job Responsibility

Configure and adapt G+D-developed algorithms for BPS systems to meet customer currency specifications
Analyze banknote data (sensor data, images, measured features) to create customer-specific adaptation profiles
Validate algorithm performance through testing, simulation, and statistical evaluation
Identify optimization opportunities along every step of the adaptation process
Use Python for exploratory data analysis, automation tasks, and algorithm evaluation
Develops/implements systems to perform basic evaluation of adaptation artifacts according to state-of-the-art quality standards
Apply statistical methods to improve adaptation quality
Build and optimize data pipelines and notebooks in Databricks to analyze large volumes of banknote data
Identify patterns, anomalies, and insights to support continuous improvement
Collaborate with algorithm development, product management, customer service, and field engineers

Fulltime

Full-Stack Data Engineer – Data & ML Automation (Databricks)

We are seeking a Fullstack Data Engineer who can operate at the intersection of ...

Location

India , Pune

Salary:

Not provided

Codvo AI

Expiration Date

Until further notice

Requirements

Experience with CLI tools, scripts, and utilities for automating data platform workflows
Experience with Databricks APIs, Terraform, Databricks SDK
Experience designing integration tests, end-to-end pipeline tests, validation frameworks for Databricks ETL/ELT pipelines and ML inference workflows
Experience building internal applications using React, Streamlit, or similar frameworks
Experience with spec-driven development, coding agents and automation patterns, CI/CD workflows for data/ML systems

Job Responsibility

Develop CLI tools, scripts, and utilities to automate repetitive workflows across the data platform
Automate Databricks workflows, job deployments, environment provisioning, and MLOps operations using Databricks APIs, Terraform, Databricks SDK
Design and implement integration tests, end-to-end pipeline tests, validation frameworks for Databricks ETL/ELT pipelines and ML inference workflows
Improve reliability, observability, and overall engineering productivity across the data & ML team
Build quick internal applications using React, Streamlit, or similar frameworks to visualize data flows, provide model inference demos, enable operational or configuration controls
Develop internal productivity and monitoring dashboards
Apply best practices around spec-driven development, coding agents and automation patterns, CI/CD workflows for data/ML systems

Fulltime

Data Engineer / Data Scientist – MLOps & Machine Learning

Location

United States , Charlotte

Salary:

70.00 USD / Hour

Beacon Hill

Expiration Date

Until further notice

Requirements

5+ years of hands-on experience as a Data Engineer or Data Scientist in large-scale, cloud-based data environments
Strong experience building or supporting MLOps workflows in Databricks
Hands-on experience with MLflow, including experiment tracking, model registry, and deployment workflows
Strong understanding of machine learning fundamentals and model lifecycle management
Solid data science foundation with understanding of ML theory, mathematics, and statistical concepts
Experience building, training, validating, and deploying machine learning models
Ability to explain model selection decisions and ML approaches clearly
Strong collaboration and communication skills with the ability to work well across cross-functional teams
Strong personality fit and ability to work effectively in team-oriented environments
Willingness to work onsite

Job Responsibility

Design, build, and support scalable machine learning and data workflows in cloud environments
Develop and maintain MLOps pipelines using Databricks and MLflow
Track, manage, and deploy machine learning models across environments
Work closely with data scientists, engineers, and business stakeholders to deliver ML-driven solutions
Analyze large datasets and apply machine learning techniques to solve business problems
Support model monitoring, optimization, and continuous improvement initiatives
Collaborate on architecture, best practices, and scalable ML engineering standards

Senior Data Engineer / Data Analyst

N-iX is a global software development service company. Our customer is the Europ...

Location

Ukraine

Salary:

Not provided

N-iX

Expiration Date

Until further notice

Requirements

4+ years of experience in analytics, sales operations, revenue insights, or data-driven consulting
Strong SQL skills (joins, window functions, performance tuning)
Production experience with Apache Airflow
Python skills for pipeline development
Ability to build and maintain database views (PostgreSQL, Snowflake, Redshift)
Solid ETL/ELT understanding
Clear communication with non-technical stakeholders
GitHub experience (branches, PRs, reviews)
English level - at least Upper-Intermediate, both spoken and written

Job Responsibility

Work with Salesforce objects and relationships to ensure correct ingestion, transformation, and integration into our reporting environment
Design, build, and maintain data pipelines using Apache Airflow (DAG creation, scheduling, monitoring, troubleshooting)
Write, optimize, and maintain SQL queries, stored procedures, and functions for data transformation and extraction
Create and manage database views to support analytics, reporting, and downstream applications
Ensure data quality, consistency, and reliability across pipelines and views (validation checks, monitoring)
Support QuickSuite dataset adjustments (new fields, logic changes, view extensions)
Document data flows, data models, and pipeline logic for long-term maintainability and handover

What we offer

Flexible working format - remote, office-based or flexible
A competitive salary and good compensation package
Personalized career growth
Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
Active tech communities with regular knowledge sharing
Education reimbursement
Memorable anniversary presents
Corporate events and team buildings
Other location-specific benefits

Senior Staff Data Engineer- Data Platform

At Marktplaats, data is at the heart of everything we do, but Intelligence is wh...

Location

Netherlands , Amsterdam

Salary:

Not provided

Adevinta

Expiration Date

Until further notice

Requirements

10+ years of hands-on experience in Software Development or Data Engineering
at least 5+ years specifically focused on building Data Platforms
deep understanding of how Platform infra supports Analytics workloads
proven experience evolving complex platforms from legacy patterns to modern, cloud-native solutions
deep knowledge of Spark internals, JVM tuning, and performance optimization for high-scale batch and streaming datasets
deep expertise in Unity Catalog, Delta Lake internals, and optimizing high-volume workloads
strict software engineering discipline (CI/CD, Testing, OOP) applied to data pipelines
understanding of microservices architecture
understanding the needs of Analytics/DWH teams (Data Modeling, dbt)
strong background in building automated pipelines using Terraform/Terragrunt and ensuring system observability

Job Responsibility

Lead the evolution of our Data Platform and architect the "Data Exchange" strategy
define robust patterns for API-based ingestion, Event-Driven Architectures (Kafka), and Reverse ETL
ensure architectures are optimized for cost and performance on AWS
act as a catalyst for technical evolution
constantly scan the horizon for next-generation technologies
lead the implementation of new paradigms
design the strategy for Unity Catalog implementation and Data Contracts
champion FinOps, automating cost controls for our highest-volume workloads
build the underlying infrastructure that allows Analytics/DWH teams to run efficient transformations
elevate the technical bar of the team, mentoring Staff and Senior engineers

What we offer

An attractive Base Salary
Participation in our Short Term Incentive plan (annual bonus)
Work From Anywhere: Enjoy up to 20 days a year of working from anywhere
A 24/7 Employee Assistance Program for you and your family
a collaborative environment with an opportunity to explore your potential and grow
a range of locally relevant benefits

Fulltime

Data Engineer-Data Scientist – MLOps & Machine Learning

Seeking a highly analytical and hands-on Data Engineer / Data Scientist with str...

Location

United States , Suffolk

Salary:

70.00 USD / Hour

Beacon Hill

Expiration Date

Until further notice

Requirements

5+ years of hands-on experience as a Data Engineer or Data Scientist in large-scale, cloud-based data environments
Strong experience building or supporting MLOps workflows in Databricks
Hands-on experience with MLflow, including experiment tracking, model registry, and deployment workflows
Strong understanding of machine learning fundamentals and model lifecycle management
Solid data science foundation with understanding of ML theory, mathematics, and statistical concepts
Experience building, training, validating, and deploying machine learning models
Ability to explain model selection decisions and ML approaches clearly
Strong collaboration and communication skills with the ability to work well across cross-functional teams
Strong personality fit and ability to work effectively in team-oriented environments
Willingness to work onsite

Job Responsibility

Design, build, and support scalable machine learning and data workflows in cloud environments
Develop and maintain MLOps pipelines using Databricks and MLflow
Track, manage, and deploy machine learning models across environments
Work closely with data scientists, engineers, and business stakeholders to deliver ML-driven solutions
Analyze large datasets and apply machine learning techniques to solve business problems
Support model monitoring, optimization, and continuous improvement initiatives
Collaborate on architecture, best practices, and scalable ML engineering standards

Fulltime

Forward Deployed Engineer - Data Migration & Data Consolidation Platforms

As a Forward Deployed Engineer (FDE) for Data Migration & Data Consolidation Pla...

Location

United States

Salary:

Not provided

Rackspace

Expiration Date

Until further notice

Requirements

7-10+ years of progressive experience in enterprise data engineering, data migration, or large-scale system integration roles within complex, multi-platform environments
3-5+ years directly leading end-to-end data migration or multi-system consolidation programs for Global Enterprises and Industry Leaders, with full ownership of technical delivery and client outcomes
Demonstrated client-facing experience serving as a trusted technical advisor to C-level executives, enterprise architecture teams, and cross-functional business stakeholders
Proven industry depth in at least two of the following verticals: Healthcare, Financial Services, Manufacturing, Retail, Energy & Utilities, or Public Sector
Hands-on migration complexity: successfully delivered programs involving at least 3+ heterogeneous source systems, 100M+ records, complex master data harmonization, and multi-phase cutover execution
Advanced proficiency in Python and SQL with working experience in PySpark and TypeScript/JavaScript
Hands-on expertise with modern ETL/ELT and data integration platforms (Informatica, Talend, Matillion, Fivetran, AWS Glue, Azure Data Factory)
Proven ability to build scalable, version-controlled data pipelines with error handling, incremental loading, and Change Data Capture (CDC)
Strong working knowledge of at least one major cloud provider (AWS, Azure, or GCP), including core infrastructure, managed data services, and security configurations
Experience with enterprise data warehouse and lakehouse platforms (Snowflake, Databricks, BigQuery, Redshift, Synapse Analytics, Delta Lake)

Job Responsibility

Migration Execution & Cloud Architecture: Lead end-to-end delivery of enterprise data migrations from corporate systems (SAP, Oracle, Epic ERP) to target cloud data platforms, including the design of cloud landing zones, data governance frameworks, and system rationalization strategies. Establish migration compliance controls, automated rollback procedures, and operational readiness gates while owning full technical accountability for 12–18+ month migration roadmaps
Data Pipeline Engineering & Transformation: Build production-grade data connectors to SAP (RFC, IDoc, BAPI, OData), Oracle (AQ, GoldenGate, APIs), and SQL/non-relational sources. Develop ETL/ELT pipelines with LLM-enabled transformation logic, multi-layer validation and reconciliation frameworks, and optimized throughput for datasets scaling from tens of millions to billions of records with built-in CDC and incremental loading
Ontology Layer Development & Schema Automation: Construct semantic ontology layers translating raw ERP structures into business-consumable objects (Customer, Order, Invoice, Product, Vendor, Asset). Deploy automated schema mapping agents for source-to-target analysis and transformation logic generation. Build unified master data models with row/column-level security, cross-system lineage tracking, and AI-ready semantic structures
Application & Workflow Delivery: Build operational dashboards, migration control centers, and agent-driven workflows for automated validation, exception handling, and anomaly detection using low-code platform tools. Generate TypeScript/Python SDKs for custom integrations and deliver real-time monitoring and self-service interfaces for migration progress, data quality KPIs, and compliance tracking
Multi-System Consolidation & Master Data Management: Lead consolidation of 5–15+ fragmented ERP instances into standardized master data models. Resolve complex entity resolution challenges including customer matching, product harmonization, and chart of accounts unification. Establish golden record frameworks, data quality scorecards, survivorship rules, and data stewardship workflows for post-migration governance
Client Engagement, Discovery & Modernization Advisory: Serve as primary technical advisor to C-suite and enterprise architecture stakeholders across all engagement phases. Deploy discovery agents to analyze legacy data estates, conduct assessment workshops, facilitate solution design sessions, and deliver executive briefings, go/no-go readiness assessments, and prioritized modernization roadmaps
Knowledge Transfer, Enablement & IP Development: Build reusable migration accelerators, playbooks, and reference architectures that scale across engagements. Lead knowledge transfer to upskill client teams for post-migration ownership and collaborate with internal product and sales engineering teams to feed field insights back into platform development and delivery methodology
Leadership & Executive Engagement: Operate autonomously in ambiguous, high-stakes client environments, driving outcomes with minimal oversight
translate deeply technical concepts into clear, business-level narratives for C-suite audiences through executive briefings and stakeholder communications
navigate organizational complexity, competing stakeholder priorities, and enterprise change management dynamics to maintain momentum across multi-workstream engagements

Senior Solutions Engineer – Big Data & Data Infrastructure

This is a great opportunity to be part of one of the fastest-growing infrastruct...

Location

Israel , Tel Aviv

Salary:

Not provided

VAST Data

Expiration Date

Until further notice

Requirements

2–4 years in software / solution or infrastructure engineering
2–4 years focused on building / maintaining large-scale data pipelines / storage & database solutions
Proficiency in Trino, Spark (Structured Streaming & batch) and solid working knowledge of Apache Kafka
Coding background in Python (must-have)
Deep understanding of data storage architectures including SQL, NoSQL, and HDFS
Solid grasp of DevOps practices, including containerization (Docker), orchestration (Kubernetes), and infrastructure provisioning (Terraform)
Experience with distributed systems, stream processing, and event-driven architecture
Hands-on familiarity with benchmarking and performance profiling for storage systems, databases, and analytics engines
Excellent communication skills

Job Responsibility

Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools
Implement event-driven and serverless workflows
Create technical guides, architecture docs, and demo pipelines
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark)
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads
Operate and debug object store–backed data lake infrastructure

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