{"id":3328774,"url":"https://alion.io/job/optimum-solutions-senior-data-engineer","title":"Senior Data Engineer","company":{"id":3800114,"name":"Optimum Solutions","domain":"theoptimum.net","url":"https://alion.io/company/optimum-infosystem","size_band":"1001-5000","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":7000,"max":10400,"currency":"SGD","period":"month","gross":true,"usd_annual":97428},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"Least Privilege","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-10-27T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-10-27T00:00:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Role Overview\nThe Senior Data Engineer is responsible for designing, building, deploying and maintaining reliable data pipelines and reusable data products on a cloud-based data platform using AWS and Databricks. The role provides technical leadership across data ingestion, transformation, modelling, quality, lineage, orchestration, observability and performance, ensuring data remains dependable for analytics, reporting and ML/AI workloads.\nKey Responsibilities\nData Pipeline Architecture and Delivery: Lead the design and implementation of maintainable batch and streaming pipelines that ingest, validate, transform and publish structured, semi-structured and unstructured data across AWS and Databricks.\nLakehouse and Data Product Engineering: Design Bronze, Silver and Gold data layers using Delta Lake or equivalent patterns; develop reusable domain data models, curated datasets and data products with explicit ownership, definitions and service expectations.\nSource Integration and Incremental Processing: Implement reliable ingestion from databases, files, APIs and event streams using full-load, incremental, change-data-capture and replayable processing patterns appropriate to each source.\nData Quality and Contracts: Establish schema enforcement, data contracts, automated validation, reconciliation and anomaly checks covering completeness, validity, consistency, uniqueness, timeliness and referential integrity.\nOrchestration and Deployment Automation: Build parameterised workflows using Databricks Workflows or Lakeflow and relevant AWS services; integrate automated testing, infrastructure as code and CI/CD controls across development, test and production environments.\nMetadata, Lineage and Governance: Implement technical metadata, business definitions, end-to-end lineage, classification, ownership and access controls using Unity Catalog, AWS Glue Data Catalog, Lake Formation or approved platform capabilities.\nPipeline Monitoring and Reliability: Define dashboards, alerts, logging, operational metrics and runbooks for pipeline health, freshness, data-quality failures, latency, throughput and downstream impact; lead troubleshooting and root-cause analysis.\nPerformance and Cost Optimisation: Optimise Spark and SQL workloads, file sizes, partitioning, clustering, caching, compaction, cluster configuration and workload scheduling to improve runtime, reliability and AWS/Databricks cost efficiency.\nSecurity and Data Protection: Apply least-privilege IAM, encryption, secrets management, environment separation, audit logging, sensitive-data controls and secure data-sharing patterns in line with applicable security and compliance requirements.\nTechnical Leadership and Quality: Set engineering standards, review designs and code, mentor engineers, manage technical debt and promote reusable components rather than project-specific pipelines.\nStakeholder Collaboration: Work with data owners, stewards, analysts, data scientists, architects, product teams and governance stakeholders to translate analytical and operational needs into implementable data specifications.\nDocumentation and Knowledge Transfer: Maintain architecture decisions, source-to-target mappings, data models, contracts, operating procedures and support documentation, and transfer knowledge to the internal team throughout the engagement.\n\nRequirements & Qualifications\nEducation: Degree in Computer Science, Software Engineering, Data Engineering, Information Systems or a related technical discipline.\nExperience: Minimum 6 years of relevant data or software engineering experience, including hands-on design and operation of production data pipelines and at least 2 years working with cloud-based data platforms. Candidates should demonstrate technical leadership through architecture, engineering standards, code review, mentoring or ownership of critical data services.\nEssential (Must-Have)\nStrong Python, PySpark and SQL capability, including distributed processing, complex transformations, query optimisation, debugging and automated testing.\nHands-on experience with Databricks, Apache Spark and Delta Lake for production-scale batch or streaming data engineering.\nHands-on AWS data engineering experience with a relevant combination of services such as S3, Glue, Lake Formation, IAM, KMS, Lambda, Step Functions, EventBridge, CloudWatch, Redshift and MSK/Kinesis.\nExperience designing lakehouse or data-lake architectures, including layered data patterns, dimensional or domain data modelling and reusable curated datasets.\nExperience implementing incremental ingestion, change data capture, idempotency, late-arriving data handling, backfills, replay and recovery.\nExperience implementing data quality, schema validation, reconciliation, metadata and lineage as automated parts of production pipelines.\nExperience with workflow orchestration, Git-based development, CI/CD, infrastructure as code such as Terraform, and environment promotion controls.\nAbility to implement pipeline observability, operational dashboards, alerting, runbooks, incident diagnosis and service recovery.\nWorking knowledge of cloud security, least-privilege access, network boundaries, encryption, secret management, data classification and audit logging.\nAbility to review technical designs and code, establish engineering standards, and guide less-experienced engineers.\n\nSkills\nData Ingestion, PySpark, Amazon S3, Apache Spark, Cloudwatch, Data Pipeline, AWS, ETL, Databricks, Data Domain, SQL, Data Lake, Python, CI/CD, Technical Architecture, integrate Data sources, AWS Glue, AWS Lambda, 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