Data Engineer (Azure | Databricks | Snowflake)
Tekwissen
Overview: TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services
Position: Data Engineer (Azure | Databricks | Snowflake) Location: Frisco, TX Duration: 6 Months Job Type: Temporary Assignment Work Type: Hybrid Job Description
Position: Data Engineer (Azure | Databricks | Snowflake) Location: Frisco, TX Duration: 6 Months Job Type: Temporary Assignment Work Type: Hybrid Job Description
- Needed for Azure-native third party data enrichment platform using Databricks/Spark + Snowflake; focus on reliable governed pipelines, strong Spark troubleshooting, privacy/governance, and cost-aware engineering; h
- Team / Business Context: You will join a data engineering team responsible for third party data enrichment augmenting first party datasets with external identity/attribute data to support analytics, activation, and research.
- The enriched datasets are consumed by multiple downstream systems and teams, including the Customer Data Platform (CDP) and other analytics/research stakeholders.
- The platform is Azure-native and built primarily on Databricks (processing + some ML workloads) and Snowflake (analytics/warehouse).
- A major focus is building reliable, governed, vendor agnostic datasets while ensuring privacy/compliance, data governance, and cost efficiency.
- As a Data Engineer, you will: Data Ingestion & Pipeline Development Build and enhance ingestion pipelines for large batch and event-driven paths (streaming may evolve over time).
- Integrate data from: Third party enrichment vendors (identity + attributes, very large volumes) Digital platforms via Conversion API (CAPI) integrations (through intermediary/middleware) Rewards/Promotions systems (e.g., TMT) for offer issuance/redemption/consumption data Data Quality, Reliability & Operations Implement strong data validation, idempotency, replay/backfill strategies, and deduplication to prevent quality drift.
- Own monitoring, alerting, dashboarding, and operational readiness ( wrappers around core pipelines).
- Troubleshoot failures with root cause analysis not just reruns: Interpret Spark logs
- Diagnose performance issues (shuffle, skew, partitioning) Improve stability and SLA adherence Governance & Compliance (First-class NFR) Apply privacy, compliance, and governance requirements across pipelines and datasets.
- Support governance standards such as: Unity Catalog, lineage, access controls Managing PII vs non PII access
- Documentation of tables, schemas, catalogs, and cluster usage Cost Governance & Performance Optimization
- Design pipelines with cost awareness from day one: Cluster sizing, workload tuning, efficient compute/storage usage
- Trade-off decisions balancing cost vs quality vs SLA Collaboration & Ownership Work in a small, fast-moving team; be self-driven and ownership-oriented.
- Raise and manage data quality escalations when issues are detected.
- Contribute to evolving architecture (product is early-stage; first live month was recent).
- Candidate with hands-on, recent experience in: Strong coding: PySpark + SQL (hands-on, not only orchestration)
- Databricks: notebooks/jobs, performance tuning fundamentals, medallion patterns Spark fundamentals: partitioning, skew/shuffle optimization, understanding failures via logs
- Snowflake: data modeling/usage for analytics/warehousing workloads Azure ecosystem: Azure Data Factory (ADF) (orchestration) Azure-native integrations and services exposure Data engineering reliability patterns: validation, idempotency, replay/backfills, dedup, auditability Data governance: Unity Catalog (preferred), lineage, access control patterns, PII handling Ownership mindset: can execute independently without constant approvals/check-ins
- Event-driven/streaming ingestion exposure (even if primary is batch today) Delta/Databricks patterns such as Delta Live Tables (DLT) (some workflows exist) Experience building config-driven export frameworks for multiple downstream consumers/vendors Exposure/interest in identity resolution concepts (ML optional; ETL strength is priority) Familiarity with CAPI integrations / marketing tech data signals
- Experience implementing operational telemetry: dashboards, alerts, SLA monitoring What Good Looks Like (Success Criteria) Ships reliable, well-governed datasets with strong data quality practices
- Can scale pipelines for very large volumes (hundreds of millions of records per vendor) Prevents silent failures where quality degrades without obvious job failures
- Balances delivery speed with compliance, governance, and cost controls
Vacancy posted 13 days ago
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