Data Engineer (Data Science / Data extraction)
- Luxoft
- 6 - 11 years
- Hyderabad
- 9 days ago
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Job Description
We''re looking for a Data Engineer with hands on experience in graph databases to design, build, and optimize data pipelines and knowledge graph solutions that power advanced analytics and discovery. You''ll collaborate with data scientists, platform engineers, and product teams to model complex domains, integrate heterogeneous sources, and deliver queryable, scalable graph data products.
Graph Data Modeling Design
Design and implement property graphs and RDF/OWL-based knowledge graphs.
Develop schemas/ontologies, entity resolution and lineage strategies; define best practices for graph modeling, naming, and versioning.
Data Engineering Integration
Build and maintain ETL/ELT pipelines to ingest, cleanse, transform, and load data into graph stores from APIs, files, RDBMS, event streams.
Implement batch and streaming integrations using tools such as Airflow, dbt, Kafka/Kinesis, Spark/Flink.
Optimize data quality, deduplication, key management, and incremental upserts into graphs.
Querying APIs
Write advanced queries in Cypher, Gremlin, and/or SPARQL; tune queries and indexes for performance.
Expose graph capabilities via APIs/services (REST/GraphQL/GRANDstack) with robust governance, observability and caching.
Performance, Reliability Security
Capacity planning, clustering, backups, and high availability for graph databases.
Monitoring/alerting (e.g., Prometheus/Grafana, CloudWatch), profiling and query plan analysis.
Apply security best practices: encryption, RBAC/ABAC, least privilege, secrets management, and data masking/Pii handling.
MLOps/Analytics Enablement (nice if applicable)
Support downstream analytics and graph algorithms (PageRank, community detection, embeddings) and integrate with ML pipelines.
DevOps SDLC
Infrastructure-as-Code (Terraform, Bicep, CloudFormation), containerization (Docker, Kubernetes), and CI/CD for data/infra.
Documentation, code reviews, and contribution to data governance (catalogs, lineage, metadata).
Must have
- Experience: 6 years in Data Engineering (or similar) with 2+ years focused on graph databases (property graph and/or RDF).
- Graph DBs: Hands-on with at least one of:
- Property Graph: Neo4j, AWS Neptune (Gremlin/Cypher).
- RDF Triple Stores: Ontotext GraphDB, Apache Jena/Fuseki, Blazegraph, Stardog, Neptune (RDF).
- Query Languages: Strong in Cypher and/or Gremlin; SPARQL if working with RDF/OWL.
- Data Pipelines: Proficient with Airflow (or similar), Kafka/Kinesis, Spark or Flink; building robust ETL/ELT at scale.
- Programming: Python (dataframes, APIs, CLI tooling); solid testing practices (pytest/pytest-bdd).
- Cloud: Experience with AWS managed graph/datastores, storage, compute, and networking basics.
- Performance Ops: Indexing, memory/GC tuning, query plan analysis, partitioning/sharding concepts, HA/DR, backup/restore.
- Security Governance: Secrets management, IAM, network isolation, PII compliance; familiarity with data catalog/lineage tools.
- Communication: Ability to translate domain knowledge into graph models and explain trade-offs to non technical stakeholders.
Nice to have
- Knowledge Graphs Semantics: RDFS, SHACL, ontology engineering, reasoning/inference, vocabulary alignment (SKOS).
- Graph Algorithms Embeddings: Neo4j Graph Data Science, NetworkX, PyTorch Geometric, vector DB integration.
- Graph + Search: Integration with Elasticsearch/OpenSearch, hybrid search (BM25 + embeddings).
- Data Modeling: Experience migrating from relational to graph; CDC patterns (Debezium), event-driven architectures.
- Observability: OpenTelemetry, tracing for data services; data quality frameworks (Great Expectations).
- Delivery: Experience with productizing graph APIs, caching layers, SLA/SLO management.
- Regulatory: Familiarity with GDPR/CCPA, data retention, sovereignty considerations.
Job Classification
Industry: LegalFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: LuxoftLocation(s): Hyderabad
Keyskills: data engineer kubernetes docker data extraction data science iam spark devops pytorch graphql cloudformation python cdc bdd airflow sla flink neo4j compliance kafka clustering terraform aws sdlc infrastructure as code
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