Data Engineer at DAG Industries Nigeria Limited

Why This Role Exists

• DAG Industries Nigeria is digitizing. You’ll build reliable data pipelines and models that turn raw data into trustworthy, analytics-ready datasets—fuelling decisions across the business in real time.

Responsibilities
What you’ll do:

• Own data pipelines end-to-end: Design, build, and maintain batch pipelines for all business units.
• Model the data: Implement robust data models that support BI, ML, and operational use cases.
• Manage storage layers: Create modern data lakes/lakehouses and warehouse schemas; ensure efficient partitioning, compaction, and cost control.
• Harden data quality: Implement tests, contracts, and observability (lineage, metadata, SLAs).
• Secure & govern: Apply best practices for IAM, data masking, encryption, and compliance with NDPR and company policies.
• Collaborate & enable: Partner with our business units to define datasets, publish documentation, and enable self-service.
• Automate the platform: Use CI/CD and Infrastructure-as-Code to provision and maintain data infrastructure reliably and use tools for workflow orchestration.
• Continuously improve: Profile workloads, tune jobs, and reduce costs while improving reliability and performance.

Must-Have Skills & Experience:

• Programming Languages: Proficiency in Python, Java, or Scala (one or more).
• Database Management: Strong SQL and hands-on experience with both relational (e.g., Postgres, MySQL, SQL Server) and NoSQL stores (e.g., MongoDB, Cassandra, DynamoDB).
• ETL/ELT Processes: Proven experience designing and operating Extract–Transform–Load workflows (batch and streaming).
• Data Modelling: Solid grasp of data modelling concepts and techniques (OLTP vs. OLAP, dimensional modelling, normalization).
• Automation: experience in creating automation with scripting and Microsoft Power Apps.
• Cloud Computing: Working knowledge of AWS, Azure, or Google Cloud services for data engineering (e.g., S3/ADLS/GCS, EMR/Databricks/Synapse/BigQuery, MSK/Kinesis/Pub/Sub).
• Big Data Technologies: Practical familiarity with Hadoop, Spark, and Kafka.
• Foundations: Git, Linux, containers; writing production-grade, testable code; clear documentation.

Nice to have:

• Airflow/Dagster orchestration expertise; dbt for transformations.
• Knowledge about lakehouse patterns (Delta/Apache Hudi/Iceberg) and columnar formats (Parquet).
• Data quality/observability tooling (Great Expectations, Monte Carlo, OpenLineage).
• CI/CD (GitHub Actions, GitLab CI, Azure DevOps) and IaC (Terraform, CloudFormation).
• BI exposure (Power BI, Looker, Tableau, Qlik Sense) and ML feature pipelines (Feast, Tecton) a plus.
• Domain experience in automotive, aftermarket, logistics, or multi-site service networks.

Requirements
Education & Experience:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
• 3–7+ years in data engineering (title/level will align with experience and scope).